Causal Relationship between Alzheimer's disease and Intracerebral hemorrhage: a two-way Mendelian randomization analysis

Causal Relationship between Alzheimer's disease and Intracerebral hemorrhage: a two-way Mendelian randomization 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 Research Article Causal Relationship between Alzheimer's disease and Intracerebral hemorrhage: a two-way Mendelian randomization analysis Hanli Wang, Yannan Che, Jianhua Gao, Yusi Huang, Linsheng Zeng, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3936524/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The relationship between Alzheimer's disease (AD) and the risk of Intracerebral hemorrhage (ICH) is still debatable. The aim of this study was to investigate the potential causal relationship between them. This study was conducted in a two-sample Mendelian randomization (MR) design using summary statistics from an independent genome-wide association study (GWAS). The selected exposure factor was AD. And different genetic predispositions for AD were categorized into two subgroups. The outcome was ICH. the main analytical tool used was the inverse variance weighting (IVW) method, supplemented by sensitivity analyses. The findings suggest a positive association between AD and ICH. The level of genetic prediction of AD was consistently positively correlated with ICH, as evidenced by IVW (odds ratio [OR] = 1.33E + 12; P < 0.01). These associations were further supported by the results of all sensitivity analyses. In addition, Illnesses of mother: Alzheimer's disease/dementia was associated with ICH (IVW: OR = 5.947; P < 0.05). AD and ICH have a unidirectional causal relationship, and illnesses of mother is a major factor with ICH, whereas Alzheimer's disease or a family history of Alzheimer's disease is not. Alzheimer's disease Intracerebral hemorrhage Mendelian randomization analysis Figures Figure 1 Figure 2 Figure 3 Introudction Alzheimer's disease (AD) is a neurodegenerative disorder that primarily manifests as cognitive impairments in memory, language, and executive functions 1 . Therefore, although these clinical manifestations may be unfamiliar to the individual, it is hypothesized that the underlying cerebral changes precipitating these symptoms might have commenced two decades or more prior to symptom onset 2,3 . According to a U.S. cohort study, it is anticipated that there will be a progressive rise in both the quantity and proportion of individuals affected by Alzheimer's disease or other forms of dementia 4 . By 2050, the prevalence will quadruple, by which time 1 in 85 persons worldwide will be living with the disease 5 . This process is linked to the aging of the population, and to the increased nutritional levels resulting from economic growth, which then leads to more metabolic diseases 6 . Alzheimer's disease is a complex and multifaceted condition, currently devoid of any curative interventions 7 . Therefore, the early identification and mitigation of risk factors play a pivotal role in decelerating the progression of Alzheimer's disease 8,9 . Stroke is a prevalent neurological disorder and ranks as the second leading cause of mortality and disability globally 10 . In a broader context, stroke can be classified into two primary categories: ischemic stroke and hemorrhagic stroke 11 . Among these subtypes, intracerebral hemorrhage (ICH) is the most severe form of stroke with a high risk of mortality, disability, and cognitive impairment, affecting 1.5 million individuals worldwide annually 10,12 . In underdeveloped countries, the incidence of hemorrhagic stroke is two times higher than that in developed countries 13 . Given that approximately 90% of stroke survivors are reportedly affected by diverse disabilities, it is imperative to elucidate the underlying etiology of ICH 13 . Alzheimer's disease (AD) and cerebral hemorrhage (ICH) share common underlying risk factors. For example, hypertension 14,15 , prehypertension (systolic 120 to 139 mmHg or diastolic 80 to 89 mmHg) 10 was associated with an increased risk of dementia in later life 16 . Furthermore, patients with ICH may develop dementia due to the ICH itself, or the risk factors for dementia are more common in patients with ICH 16 . In Mendelian randomization (MR), a novel approach in epidemiological research, genetic variation is employed as instrumental variables (IVs) to infer causal relationships between exposure factors and outcomes 17 . Moreover, the IVs employed in MR analysis are gene assignments occurring randomly during meiosis, resulting in the stochastic distribution of genetic variants within the population 17 . Hence, the utilization of MR analysis can effectively mitigate the influence of conventional confounding factors and adhere to the inherent causal sequence 18 . Furthermore, the development of genome-wide association studies (GWAS) and the availability of independent GWAS databases offer robust instrumental variables for MR analysis 19,20 . Mendelian studies have been extensively employed in investigating the association between AD and stroke. MR studies have established that insulin resistance 21 and frailty index 22 are shared risk factors for both conditions. The genetic evidence from a MR study conducted in 2020 supports the hypothesis that there is no causal association between different subtypes of ischemic stroke and AD 23 . This study employed a two-way MR analysis to investigate the causal relationship between AD, illnesses of mother, and AD or family history of AD, as well as their association with ICH. MR analysis results AD and ICH A summary of the results from the Mendelian randomization (MR) analysis is presented in Table 1 . The comparison between AD and ICH revealed a significant causal relationship between the two conditions (IVW: odds ratio [OR] = 1.33E + 12; 95% confidence interval [CI], 1.45E + 03-1.22E + 21; P<0.01). Figure 1 A illustrates a scatter plot depicting the effect size of the association between AD and ICH in the MR analysis. Figure 1 B: Leave-one-out analysis of the causal effect of AD on ICH. Each black point represents the IVW MR method applied to estimate the causal effect of adiponectin level on disorders of vitreous body, excluding that particular variant from the analysis. The red point represents the IVW estimate using all SNPs. To further validate the robustness of the causal relationship between AD and ICH, a sensitivity analysis was conducted. No outliers were detected in the leave-one-out analysis (Fig. 1 B). Moreover, the heterogeneity test confirmed significant heterogeneity in IVW (P = 0.305), necessitating the utilization of a random-effects model (P = 0.008). To elucidate the temporal relationship between these two diseases, we further developed an inverse model to estimate the impact of intracerebral hemorrhage (ICH) on AD. Importantly, our analysis revealed no significant association (P = 0.665). This Mendelian randomization analysis unequivocally establishes AD as a risk factor for cerebral hemorrhage, while indicating that cerebral hemorrhage is not causally linked to AD. Table 1 Effect of AD on ICH Expousure Outcome SNP method OR 95% [CI] P AD ICH 4 MR Egger 3.51E + 14 1.24E-01-9.90E + 29 0.206 Weighted median 2.00E + 11 9.00E + 02-4.45E + 19 <0.01 Inverse variance weighted 1.33E + 12 1.45E + 03–1.22E + 21 <0.01 Simple mode 1.01E + 11 1.29E-08–7.83E + 29 0.337 Weighted mode 1.63E + 11 4.79E + 02–5.52E + 19 0.082 Association of different subgroups of AD with ICH Based on the aforementioned findings, we employed diverse algorithms to evaluate the impact of AD on ICH, yielding inconclusive outcomes. Consequently, we stratified AD into two subgroups with distinct genetic predispositions to elucidate its effect on ICH further. The results of the Mendelian randomization (MR) analysis are presented in Table 2 . Maternal illnesses, specifically Alzheimer's disease/dementia, exhibited a significant correlation with cerebral hemorrhage (IVW: OR = 5.947; 95% CI, 1.165–30.356; P = 0.032). Notably, the heterogeneity test revealed substantial heterogeneity within the IVW analysis (P = 0.210), prompting us to employ a random-effects model (P = 0.032). Table 2 Effect of other types of AD Traits on ICH Expousure Outcome SNPs Methods OR 95% CI P Illnesses of mother: Alzheimer's disease/dementia ICH 8 MR Egger 9.757 1.212–78.570 0.076 Weighted median 7.243 1.731–30.313 <0.01 Inverse variance weighted 5.947 1.165–30.356 <0.05 Simple mode 4.635 0.037–586.952 0.554 Weighted mode 7.231 1.550–33.731 <0.05 Alzheimer's disease or family history of Alzheimer's disease 36 MR Egger / / / Weighted median 1.059 0.931–1.206 0.382 Inverse variance weighted 1.027 0.941–1.120 0.551 Simple mode 1.026 0.822–1.280 0.825 Weighted mode 1.065706 0.9400291–1.208185 0.327 To ensure result stability, a leave-one-out analysis was conducted for this specific investigation, yielding consistent outcomes without any identified outliers (Fig. 2 A). Figure 2 B presents a scatter plot of the MR analysis, illustrating the effect size of the association between maternal illnesses (Alzheimer's disease/dementia) and ICH. In contrast, no significant causal relationship was observed between cerebral hemorrhage and either Alzheimer's disease or family history of Alzheimer's disease (P = 0.551). Discussion We conducted a bidirectional MR analysis on the latest 2022 AD sample to establish a more plausible causal relationship between AD and ICH. Our MR study provides compelling evidence of a direct causal association between AD and ICH. Additionally, we observed a significant causal link between Illnesses of mother and ICH. However, we were unable to establish a causal relationship between AD and most subtypes of ICH. These findings contribute to our understanding of the impact and potential genetic pathways underlying the development of cerebrovascular diseases associated with AD. AD and ICH share common underlying risk factors in their pathogenesis 24,25,26 . For instance, prehypertension is associated with an increased susceptibility to dementia in later stages of life 27,28,29 . In addition, patients with ICH may exhibit a higher propensity for developing dementia either due to the direct impact of ICH or due to an increased prevalence of risk factors associated with dementia in this patient population 30,31 . Clinical studies have revealed that patients with ICH may exhibit a higher propensity for developing dementia, either as a direct consequence of ICH itself or due to the presence of risk factors associated with dementia 16 . The risk of dementia and cognitive impairment following ICH is significantly elevated compared to the general population or even survivors of acute ischemic stroke, with dementia rates reaching as high as 20.7% within the first year post-ICH and up to 30% within a span of 45 years 32,33 . Based on the aforementioned research foundation, scholars have commenced investigating the genetic perspective of the interconnection between these two factors. The apolipoprotein E type 4 allele (APOE-epsilon 4) has demonstrated its capacity to modify both amyloid β protein aggregation and clearance. Associated with lobar ICH in Caucasian populations and a major genetic risk factor for AD and lobar ICH 34,35,36 . A recent study provides further evidence of the presence of common high-risk genes in both populations, as supported by SNP analysis 16 . However, the sequence of both events could not be determined due to numerous confounding factors. By employing Mendelian randomization, we can ascertain that AD serves as a significant risk factor for ICH; however, the reverse relationship does not hold true. The classification of ICH into traumatic and spontaneous subtypes may account for the negative results observed in our MR analysis. Consequently, it is important to acknowledge this study's limitation in terms of its inability to conduct subgroup analysis on the cerebral hemorrhage dataset based on traumatic versus spontaneous cases. Subsequently, we conducted a comprehensive analysis of AD from various genetic perspectives. Notably, there exists a causal relationship between individuals whose mothers have AD and the occurrence of ICH, while no significant association was observed between individuals with a family history of AD and ICH. It is important to note that the presence of a family history of Alzheimer's does not serve as an obligatory factor for an individual to develop the disease; however, those who have had a parent or sibling (first-degree relative) affected by AD are at higher risk compared to first-degree relatives without any history of the disease 37 . Individuals with a familial history of Alzheimer's disease involving more than one first-degree relative are at an elevated risk 38 . A population-based study of considerable magnitude revealed that the presence of a parent with dementia significantly elevates the risk, irrespective of established genetic risk factors such as APOE-e4 39 . When diseases exhibit familial patterns, both genetic and shared non-genetic factors (such as access to nutritious foods and engagement in physical activity-related habits) may contribute. The MR analyses in this paper possess several advantages. Firstly, we utilized a large-scale pooled dataset from GWAS on cerebral hemorrhage and the 2022 AD GWAS pooled dataset. Additionally, the instrumental variables used for different MR analyses are located in distinct genes, with no demonstrated impact of linkage disequilibrium on potential association analyses. Moreover, three MR methods were employed in this study to enhance the robustness of the results. Several pleiotropic analyses were also conducted to mitigate the influence of pleiotropy on MR outcomes. Furthermore, a sensitivity analysis using the leave-one-out method was performed to ensure result stability in our study. Lastly, further MR investigations were carried out considering diverse genetic histories of AD and cerebral hemorrhage. Our MR study also has certain limitations. Firstly, we did not subgroup the brain hemorrhage dataset for analysis based on traumatic and spontaneous cases, potentially leading to an incomplete capture of other genetic variants associated with cerebral hemorrhage that may have been identified in previous studies. Therefore, it is recommended to utilize another stroke GWAS pooled dataset to identify additional genetic variations as instrumental variables in future MR studies. Secondly, while participants in the AD GWAS pooled dataset were of European ancestry, subjects included in the stroke GWAS pooled dataset represented diverse ancestral backgrounds including European and non-European ancestries. Hence, population stratification should be considered due to its potential implications on possible associations. Materials and methods Study design In this study, AD and its various familial histories were considered as subgroups, serving as the 'exposures', while Intracerebral hemorrhage was regarded as the 'outcome'. IVs were subsequently screened for bidirectional MR analysis. The heterogeneity was assessed using Cochran's Q-analysis. Subsequently, the reliability of causality was verified through sensitivity analyses, including horizontal multiplicity analysis and "leave-one-out" analysis. Reversed MR analysis was also conducted to ascertain the temporal sequence between ICH and AD. The present study employed a two-way Mendelian randomization approach to evaluate the causal association between AD and ICH, while ensuring adherence to three fundamental assumptions: ( 1 ) strong genetic variant/instrumental variable (IV) associations with exposure factors; ( 2 ) independence of genetic variants/IVs from confounding factors influencing both exposure factors and outcomes; and ( 3 ) exclusivity of IVs' impact on outcomes solely through exposure rather than alternative pathways (Fig. 3 ). Sources of data The data utilized in this study were acquired from the Medical Research Council Integrated Epidemiology Unit Open GWAS database ( https://gwas.mrcieu.ac.uk/ ). The AD (ieu-b-5067) dataset was examined as an exposure variable, while the Intracerebral hemorrhage (ebi-a-GCST90018870) dataset was assessed as an outcome measure. Table 3 Description of FinnGen Statistics for vitreous disorders Trait Year Author Sex GWAS ID Sample size Number of SNPs Population Alzheimer's disease 2022 Benjamin Woolf Males and Females ieu-b-5067 488,285 12,321,875 European Illnesses of mother: Alzheimer's disease/dementia 2018 Ben Elsworth Males and Females ukb-b-14699 423,738 9,851,867 Alzheimer's disease or family history of Alzheimer's disease 2021 Schwartzentruber J NA ebi-a-GCST90012877 472,868 10,602,762 Intracerebral hemorrhage 2021 Sakaue S NA ebi-a-GCST90018870 473,513 24,191,284 Moreover, maternal disease related to illnesses of mother (ukb-b-14699), along with Alzheimer's disease or family history of Alzheimer's disease (ebi-a-GCST90012877), were considered as exposure factors in this study. For the MR analysis, only genetic variants meeting the threshold of genome-wide significance (P < 5×10 − 8 ) were included. The characteristics of each outcome factor are comprehensively described in Table 3 . The studies included in the open dataset were approved by their respective institutional review boards. Written informed consent was obtained from all participants of European descent. Mendelian randomization analysis The MR analyses employed a fixed-effects inverse variance weighting (IVW) approach to derive causal estimates of the genetic prediction of Alzheimer's disease on the outcome. Sensitivity analyses, including the weighted median and Mr. Egger's various approaches, were conducted 40,41 . The weighted median approach assumes that a minimum of 50% of the information is derived from a valid independent variable (IV) 40 . Despite the wide range of confidence intervals, the MR-Egger method offers a validated approach that enables genetic variation to nullify any potential biases 41 . For primary analyses, statistical significance was defined as P values < 0.005 (accounting for multiple tests across 10 outcomes). Associations with P values between 0.005 and 0.05 were considered suggestive of potential associations. In secondary analyses focusing on AD, a more stringent significance threshold of P < 0.025 (adjusted for multiple testing) was applied, with associations between 0.025 and 0.05 considered suggestive of potential associations. Conclusion AD and ICH have a unidirectional causal relationship, and illnesses of mother is a major factor with ICH, whereas Alzheimer's disease or a family history of Alzheimer's disease is not. Whether AD and different genetic family history have an effect on cerebral hemorrhage subtypes requires further study. Declarations i. Ethical Approval and consent to participate Not applicable. ii. Consent to Publication All authors approved the final manuscript and the submission to this journa. iii. Data Availability statement The exposure datasets generated during the current study are available in the MR-base repository, [https://gwas.mrcieu.ac.uk/]. The supplementary material used and/or analyzed during the current study are available from the corresponding author upon reasonable request. iv. Conflict of interest All authors declare that there's no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. v. Funding This work was supported by the National Natural Science Foundation of China (82204983), Shenzhen Science and Technology Program (JCYJ20220531092010023). vi. Author contribution All authors reviewed the manuscript. ZZM, LLW and YXL conceived and organized the study. HLW, YNC and JHG contributes to the dataset. HLW, YSH, XYL, ZYZ and LSZ conducted Mendelian randomization analysis. The manuscript was written by HLW and corrected by ZZM. All authors contributed to the data and text in this article. References 2023 Alzheimer’s disease facts and figures. Alzheimers Dement. J. Alzheimers Assoc. 19, 1598–1695 (2023). Quiroz YT, et al. 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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-3936524","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273837997,"identity":"2392c9b5-5970-4d49-924c-50aa65d6187a","order_by":0,"name":"Hanli Wang","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hanli","middleName":"","lastName":"Wang","suffix":""},{"id":273837998,"identity":"d2ca7c2c-1d5e-4b04-9a36-d841033573c2","order_by":1,"name":"Yannan Che","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yannan","middleName":"","lastName":"Che","suffix":""},{"id":273837999,"identity":"82adbb32-25c9-4b04-b06b-723fc02a34ea","order_by":2,"name":"Jianhua Gao","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianhua","middleName":"","lastName":"Gao","suffix":""},{"id":273838000,"identity":"cc0ab51f-98e6-4870-ba17-95646c83840e","order_by":3,"name":"Yusi Huang","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yusi","middleName":"","lastName":"Huang","suffix":""},{"id":273838001,"identity":"8af2cc21-0644-48db-95f3-67e391d731ad","order_by":4,"name":"Linsheng Zeng","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Linsheng","middleName":"","lastName":"Zeng","suffix":""},{"id":273838002,"identity":"14c955a2-a29e-44fb-861f-f61afaae85f9","order_by":5,"name":"Xianyong Liao","email":"","orcid":"","institution":"The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xianyong","middleName":"","lastName":"Liao","suffix":""},{"id":273838003,"identity":"53e02ce7-2803-4cc8-bd75-83890e643a0b","order_by":6,"name":"Zhongyi Zeng","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhongyi","middleName":"","lastName":"Zeng","suffix":""},{"id":273838004,"identity":"7d5e7436-740e-4cd8-83aa-e5c3c09d1d08","order_by":7,"name":"Lingling Wen","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lingling","middleName":"","lastName":"Wen","suffix":""},{"id":273838005,"identity":"8bc6dded-515c-4030-b129-ef1f79a36a43","order_by":8,"name":"Yuxiang Liu","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuxiang","middleName":"","lastName":"Liu","suffix":""},{"id":273838006,"identity":"c4900173-69ba-4e85-aef4-85661b668ec6","order_by":9,"name":"Zhizhun Mo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIie3PoQ6CQBzH8T9FDTzAMSZvwIZjI/Is9x+BajQeY4NXYPMlSDrbudug8AA4DViwEgkEsbq5O5rhvvn3CT8Ane4/WwEFDrBOkm5YRkyR7gplAh9C4szeqOzdPO+7bnrg2XoyGyB0XCYhQcNjj5o9Xo7I/D1EfsBlpKUVoURgeUcWFcDxJCeYEerN5HZlwlQj0YpQOpPWSFI10lQz4cIvG0yNwlP5Uue9NU5iW9b1axwOoSMlX3nL5jqdTqf70RugSUyXxlgrYQAAAABJRU5ErkJggg==","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhizhun","middleName":"","lastName":"Mo","suffix":""}],"badges":[],"createdAt":"2024-02-07 10:36:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3936524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3936524/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51503429,"identity":"363ee978-d267-4ca9-b999-f44f0281ff0f","added_by":"auto","created_at":"2024-02-22 18:06:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":670858,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA: Scatterplots for MR analyses of the causal effect ofAD on ICH. The slope of each line corresponds to the estimated MR effect per method.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB: Leave-one-out analysis of the causal effect of AD on ICH. Each black point represents the IVW MR method applied to estimate the causal effect of adiponectin level on disorders of vitreous body, excluding that particular variant from the analysis. The red point represents the IVW estimate using all SNPs.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3936524/v1/4d70899ea60edd59a1f36b93.png"},{"id":51503034,"identity":"2882ea0e-34a2-4055-8685-257d2438a233","added_by":"auto","created_at":"2024-02-22 17:58:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":931154,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA: Leave-one-out analysis of the causal effect of Illnesses of mother: Alzheimer's disease/dementia on ICH.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB: Scatterplots for MR analyses of the causal effect of Illnesses of mother: Alzheimer's disease/dementia on ICH.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3936524/v1/1f3fce8462948b3a1af4d8d8.png"},{"id":51503037,"identity":"7a857c10-d8e4-4550-a336-727d76f5efff","added_by":"auto","created_at":"2024-02-22 17:58:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65139,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation of the study methodology.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3936524/v1/32ac8e5f4f9e177caaff12c6.png"},{"id":67397539,"identity":"0558847d-4baa-4b2d-aa62-8c6f75697914","added_by":"auto","created_at":"2024-10-24 12:23:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2215596,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3936524/v1/3756764e-d090-4fb2-8802-91e87cba805b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Relationship between Alzheimer's disease and Intracerebral hemorrhage: a two-way Mendelian randomization analysis","fulltext":[{"header":"Introudction","content":"\u003cp\u003eAlzheimer's disease (AD) is a neurodegenerative disorder that primarily manifests as cognitive impairments in memory, language, and executive functions\u003csup\u003e1\u003c/sup\u003e. Therefore, although these clinical manifestations may be unfamiliar to the individual, it is hypothesized that the underlying cerebral changes precipitating these symptoms might have commenced two decades or more prior to symptom onset\u003csup\u003e2,3\u003c/sup\u003e. According to a U.S. cohort study, it is anticipated that there will be a progressive rise in both the quantity and proportion of individuals affected by Alzheimer's disease or other forms of dementia\u003csup\u003e4\u003c/sup\u003e. By 2050, the prevalence will quadruple, by which time 1 in 85 persons worldwide will be living with the disease\u003csup\u003e5\u003c/sup\u003e. This process is linked to the aging of the population, and to the increased nutritional levels resulting from economic growth, which then leads to more metabolic diseases\u003csup\u003e6\u003c/sup\u003e. Alzheimer's disease is a complex and multifaceted condition, currently devoid of any curative interventions\u003csup\u003e7\u003c/sup\u003e. Therefore, the early identification and mitigation of risk factors play a pivotal role in decelerating the progression of Alzheimer's disease\u003csup\u003e8,9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStroke is a prevalent neurological disorder and ranks as the second leading cause of mortality and disability globally\u003csup\u003e10\u003c/sup\u003e. In a broader context, stroke can be classified into two primary categories: ischemic stroke and hemorrhagic stroke\u003csup\u003e11\u003c/sup\u003e. Among these subtypes, intracerebral hemorrhage (ICH) is the most severe form of stroke with a high risk of mortality, disability, and cognitive impairment, affecting 1.5\u0026nbsp;million individuals worldwide annually\u003csup\u003e10,12\u003c/sup\u003e. In underdeveloped countries, the incidence of hemorrhagic stroke is two times higher than that in developed countries\u003csup\u003e13\u003c/sup\u003e. Given that approximately 90% of stroke survivors are reportedly affected by diverse disabilities, it is imperative to elucidate the underlying etiology of ICH\u003csup\u003e13\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlzheimer's disease (AD) and cerebral hemorrhage (ICH) share common underlying risk factors. For example, hypertension\u003csup\u003e14,15\u003c/sup\u003e, prehypertension (systolic 120 to 139 mmHg or diastolic 80 to 89 mmHg)\u003csup\u003e10\u003c/sup\u003ewas associated with an increased risk of dementia in later life\u003csup\u003e16\u003c/sup\u003e. Furthermore, patients with ICH may develop dementia due to the ICH itself, or the risk factors for dementia are more common in patients with ICH\u003csup\u003e16\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eIn Mendelian randomization (MR), a novel approach in epidemiological research, genetic variation is employed as instrumental variables (IVs) to infer causal relationships between exposure factors and outcomes\u003csup\u003e17\u003c/sup\u003e. Moreover, the IVs employed in MR analysis are gene assignments occurring randomly during meiosis, resulting in the stochastic distribution of genetic variants within the population\u003csup\u003e17\u003c/sup\u003e. Hence, the utilization of MR analysis can effectively mitigate the influence of conventional confounding factors and adhere to the inherent causal sequence\u003csup\u003e18\u003c/sup\u003e. Furthermore, the development of genome-wide association studies (GWAS) and the availability of independent GWAS databases offer robust instrumental variables for MR analysis\u003csup\u003e19,20\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMendelian studies have been extensively employed in investigating the association between AD and stroke. MR studies have established that insulin resistance\u003csup\u003e21\u003c/sup\u003e and frailty index\u003csup\u003e22\u003c/sup\u003e are shared risk factors for both conditions. The genetic evidence from a MR study conducted in 2020 supports the hypothesis that there is no causal association between different subtypes of ischemic stroke and AD\u003csup\u003e23\u003c/sup\u003e. This study employed a two-way MR analysis to investigate the causal relationship between AD, illnesses of mother, and AD or family history of AD, as well as their association with ICH.\u003c/p\u003e"},{"header":"MR analysis results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAD and ICH\u003c/h2\u003e \u003cp\u003eA summary of the results from the Mendelian randomization (MR) analysis is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The comparison between AD and ICH revealed a significant causal relationship between the two conditions (IVW: odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.33E\u0026thinsp;+\u0026thinsp;12; 95% confidence interval [CI], 1.45E\u0026thinsp;+\u0026thinsp;03-1.22E\u0026thinsp;+\u0026thinsp;21; P\u0026lt;0.01). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA illustrates a scatter plot depicting the effect size of the association between AD and ICH in the MR analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB: \u003cb\u003eLeave-one-out analysis of the causal effect of AD on ICH. Each black point represents the IVW MR method applied to estimate the causal effect of adiponectin level on disorders of vitreous body, excluding that particular variant from the analysis. The red point represents the IVW estimate using all SNPs.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo further validate the robustness of the causal relationship between AD and ICH, a sensitivity analysis was conducted. No outliers were detected in the leave-one-out analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Moreover, the heterogeneity test confirmed significant heterogeneity in IVW (P\u0026thinsp;=\u0026thinsp;0.305), necessitating the utilization of a random-effects model (P\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e \u003cp\u003eTo elucidate the temporal relationship between these two diseases, we further developed an inverse model to estimate the impact of intracerebral hemorrhage (ICH) on AD. Importantly, our analysis revealed no significant association (P\u0026thinsp;=\u0026thinsp;0.665). This Mendelian randomization analysis unequivocally establishes AD as a risk factor for cerebral hemorrhage, while indicating that cerebral hemorrhage is not causally linked to AD.\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\u003eEffect of AD on ICH\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpousure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% [CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eICH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.51E\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.24E-01-9.90E\u0026thinsp;+\u0026thinsp;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.00E\u0026thinsp;+\u0026thinsp;02-4.45E\u0026thinsp;+\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.45E\u0026thinsp;+\u0026thinsp;03\u0026ndash;1.22E\u0026thinsp;+\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.29E-08\u0026ndash;7.83E\u0026thinsp;+\u0026thinsp;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.63E\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.79E\u0026thinsp;+\u0026thinsp;02\u0026ndash;5.52E\u0026thinsp;+\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of different subgroups of AD with ICH\u003c/h2\u003e \u003cp\u003eBased on the aforementioned findings, we employed diverse algorithms to evaluate the impact of AD on ICH, yielding inconclusive outcomes. Consequently, we stratified AD into two subgroups with distinct genetic predispositions to elucidate its effect on ICH further.\u003c/p\u003e \u003cp\u003eThe results of the Mendelian randomization (MR) analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Maternal illnesses, specifically Alzheimer's disease/dementia, exhibited a significant correlation with cerebral hemorrhage (IVW: OR\u0026thinsp;=\u0026thinsp;5.947; 95% CI, 1.165\u0026ndash;30.356; P\u0026thinsp;=\u0026thinsp;0.032). Notably, the heterogeneity test revealed substantial heterogeneity within the IVW analysis (P\u0026thinsp;=\u0026thinsp;0.210), prompting us to employ a random-effects model (P\u0026thinsp;=\u0026thinsp;0.032).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of other types of AD Traits on ICH\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" 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\u003eExpousure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMethods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eIllnesses of mother: Alzheimer's disease/dementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eICH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.212\u0026ndash;78.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.731\u0026ndash;30.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.165\u0026ndash;30.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u0026ndash;586.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.550\u0026ndash;33.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAlzheimer's disease or family history of Alzheimer's disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.931\u0026ndash;1.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.941\u0026ndash;1.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.822\u0026ndash;1.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.065706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9400291\u0026ndash;1.208185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.327\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\u003eTo ensure result stability, a leave-one-out analysis was conducted for this specific investigation, yielding consistent outcomes without any identified outliers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB presents a scatter plot of the MR analysis, illustrating the effect size of the association between maternal illnesses (Alzheimer's disease/dementia) and ICH.\u003c/p\u003e \u003cp\u003eIn contrast, no significant causal relationship was observed between cerebral hemorrhage and either Alzheimer's disease or family history of Alzheimer's disease (P\u0026thinsp;=\u0026thinsp;0.551).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted a bidirectional MR analysis on the latest 2022 AD sample to establish a more plausible causal relationship between AD and ICH. Our MR study provides compelling evidence of a direct causal association between AD and ICH. Additionally, we observed a significant causal link between Illnesses of mother and ICH. However, we were unable to establish a causal relationship between AD and most subtypes of ICH. These findings contribute to our understanding of the impact and potential genetic pathways underlying the development of cerebrovascular diseases associated with AD.\u003c/p\u003e \u003cp\u003eAD and ICH share common underlying risk factors in their pathogenesis\u003csup\u003e24,25,26\u003c/sup\u003e. For instance, prehypertension is associated with an increased susceptibility to dementia in later stages of life\u003csup\u003e27,28,29\u003c/sup\u003e. In addition, patients with ICH may exhibit a higher propensity for developing dementia either due to the direct impact of ICH or due to an increased prevalence of risk factors associated with dementia in this patient population\u003csup\u003e30,31\u003c/sup\u003e. Clinical studies have revealed that patients with ICH may exhibit a higher propensity for developing dementia, either as a direct consequence of ICH itself or due to the presence of risk factors associated with dementia\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe risk of dementia and cognitive impairment following ICH is significantly elevated compared to the general population or even survivors of acute ischemic stroke, with dementia rates reaching as high as 20.7% within the first year post-ICH and up to 30% within a span of 45 years\u003csup\u003e32,33\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBased on the aforementioned research foundation, scholars have commenced investigating the genetic perspective of the interconnection between these two factors. The apolipoprotein E type 4 allele (APOE-epsilon 4) has demonstrated its capacity to modify both amyloid β protein aggregation and clearance. Associated with lobar ICH in Caucasian populations and a major genetic risk factor for AD and lobar ICH\u003csup\u003e34,35,36\u003c/sup\u003e. A recent study provides further evidence of the presence of common high-risk genes in both populations, as supported by SNP analysis\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the sequence of both events could not be determined due to numerous confounding factors. By employing Mendelian randomization, we can ascertain that AD serves as a significant risk factor for ICH; however, the reverse relationship does not hold true. The classification of ICH into traumatic and spontaneous subtypes may account for the negative results observed in our MR analysis. Consequently, it is important to acknowledge this study's limitation in terms of its inability to conduct subgroup analysis on the cerebral hemorrhage dataset based on traumatic versus spontaneous cases.\u003c/p\u003e \u003cp\u003eSubsequently, we conducted a comprehensive analysis of AD from various genetic perspectives. Notably, there exists a causal relationship between individuals whose mothers have AD and the occurrence of ICH, while no significant association was observed between individuals with a family history of AD and ICH. It is important to note that the presence of a family history of Alzheimer's does not serve as an obligatory factor for an individual to develop the disease; however, those who have had a parent or sibling (first-degree relative) affected by AD are at higher risk compared to first-degree relatives without any history of the disease\u003csup\u003e37\u003c/sup\u003e. Individuals with a familial history of Alzheimer's disease involving more than one first-degree relative are at an elevated risk\u003csup\u003e38\u003c/sup\u003e. A population-based study of considerable magnitude revealed that the presence of a parent with dementia significantly elevates the risk, irrespective of established genetic risk factors such as APOE-e4\u003csup\u003e39\u003c/sup\u003e. When diseases exhibit familial patterns, both genetic and shared non-genetic factors (such as access to nutritious foods and engagement in physical activity-related habits) may contribute.\u003c/p\u003e \u003cp\u003eThe MR analyses in this paper possess several advantages. Firstly, we utilized a large-scale pooled dataset from GWAS on cerebral hemorrhage and the 2022 AD GWAS pooled dataset. Additionally, the instrumental variables used for different MR analyses are located in distinct genes, with no demonstrated impact of linkage disequilibrium on potential association analyses. Moreover, three MR methods were employed in this study to enhance the robustness of the results. Several pleiotropic analyses were also conducted to mitigate the influence of pleiotropy on MR outcomes. Furthermore, a sensitivity analysis using the leave-one-out method was performed to ensure result stability in our study. Lastly, further MR investigations were carried out considering diverse genetic histories of AD and cerebral hemorrhage.\u003c/p\u003e \u003cp\u003eOur MR study also has certain limitations. Firstly, we did not subgroup the brain hemorrhage dataset for analysis based on traumatic and spontaneous cases, potentially leading to an incomplete capture of other genetic variants associated with cerebral hemorrhage that may have been identified in previous studies. Therefore, it is recommended to utilize another stroke GWAS pooled dataset to identify additional genetic variations as instrumental variables in future MR studies. Secondly, while participants in the AD GWAS pooled dataset were of European ancestry, subjects included in the stroke GWAS pooled dataset represented diverse ancestral backgrounds including European and non-European ancestries. Hence, population stratification should be considered due to its potential implications on possible associations.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eIn this study, AD and its various familial histories were considered as subgroups, serving as the 'exposures', while Intracerebral hemorrhage was regarded as the 'outcome'. IVs were subsequently screened for bidirectional MR analysis. The heterogeneity was assessed using Cochran's Q-analysis. Subsequently, the reliability of causality was verified through sensitivity analyses, including horizontal multiplicity analysis and \"leave-one-out\" analysis. Reversed MR analysis was also conducted to ascertain the temporal sequence between ICH and AD.\u003c/p\u003e \u003cp\u003eThe present study employed a two-way Mendelian randomization approach to evaluate the causal association between AD and ICH, while ensuring adherence to three fundamental assumptions: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) strong genetic variant/instrumental variable (IV) associations with exposure factors; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) independence of genetic variants/IVs from confounding factors influencing both exposure factors and outcomes; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) exclusivity of IVs' impact on outcomes solely through exposure rather than alternative pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSources of data\u003c/h3\u003e\n\u003cp\u003eThe data utilized in this study were acquired from the Medical Research Council Integrated Epidemiology Unit Open GWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The AD (ieu-b-5067) dataset was examined as an exposure variable, while the Intracerebral hemorrhage (ebi-a-GCST90018870) dataset was assessed as an outcome measure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of FinnGen Statistics for vitreous disorders\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNumber of SNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlzheimer's disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenjamin Woolf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eieu-b-5067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e488,285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12,321,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIllnesses of mother: Alzheimer's disease/dementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBen Elsworth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eukb-b-14699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e423,738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlzheimer's disease or family history of Alzheimer's disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSchwartzentruber J\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eebi-a-GCST90012877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e472,868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10,602,762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntracerebral hemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSakaue S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eebi-a-GCST90018870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e473,513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24,191,284\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\u003eMoreover, maternal disease related to illnesses of mother (ukb-b-14699), along with Alzheimer's disease or family history of Alzheimer's disease (ebi-a-GCST90012877), were considered as exposure factors in this study. For the MR analysis, only genetic variants meeting the threshold of genome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) were included. The characteristics of each outcome factor are comprehensively described in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The studies included in the open dataset were approved by their respective institutional review boards. Written informed consent was obtained from all participants of European descent.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMendelian randomization analysis\u003c/h2\u003e \u003cp\u003eThe MR analyses employed a fixed-effects inverse variance weighting (IVW) approach to derive causal estimates of the genetic prediction of Alzheimer's disease on the outcome. Sensitivity analyses, including the weighted median and Mr. Egger's various approaches, were conducted\u003csup\u003e40,41\u003c/sup\u003e. The weighted median approach assumes that a minimum of 50% of the information is derived from a valid independent variable (IV)\u003csup\u003e40\u003c/sup\u003e. Despite the wide range of confidence intervals, the MR-Egger method offers a validated approach that enables genetic variation to nullify any potential biases\u003csup\u003e41\u003c/sup\u003e. For primary analyses, statistical significance was defined as P values\u0026thinsp;\u0026lt;\u0026thinsp;0.005 (accounting for multiple tests across 10 outcomes). Associations with P values between 0.005 and 0.05 were considered suggestive of potential associations. In secondary analyses focusing on AD, a more stringent significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.025 (adjusted for multiple testing) was applied, with associations between 0.025 and 0.05 considered suggestive of potential associations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAD and ICH have a unidirectional causal relationship, and illnesses of mother is a major factor with ICH, whereas Alzheimer's disease or a family history of Alzheimer's disease is not. Whether AD and different genetic family history have an effect on cerebral hemorrhage subtypes requires further study.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003ei. Ethical Approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eii. Consent to Publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved the final manuscript and the submission to this journa.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eiii. Data Availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe exposure datasets generated during the current study are available in the MR-base repository, [https://gwas.mrcieu.ac.uk/]. The supplementary material used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eiv. Conflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that there\u0026apos;s no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ev. Funding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82204983), Shenzhen Science and Technology Program (JCYJ20220531092010023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003evi. Author contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors reviewed the manuscript. ZZM, LLW and YXL conceived and organized the study. HLW, YNC and JHG contributes to the dataset. HLW, YSH, XYL, ZYZ and LSZ conducted Mendelian randomization analysis. The manuscript was written by HLW and corrected by ZZM. All authors contributed to the data and text in this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e2023 Alzheimer\u0026rsquo;s disease facts and figures. \u003cem\u003eAlzheimers Dement. J. 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Int J Epidemiol. 2015;44:512\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer's disease, Intracerebral hemorrhage, Mendelian randomization analysis","lastPublishedDoi":"10.21203/rs.3.rs-3936524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3936524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe relationship between Alzheimer's disease (AD) and the risk of Intracerebral hemorrhage (ICH) is still debatable. The aim of this study was to investigate the potential causal relationship between them. This study was conducted in a two-sample Mendelian randomization (MR) design using summary statistics from an independent genome-wide association study (GWAS). The selected exposure factor was AD. And different genetic predispositions for AD were categorized into two subgroups. The outcome was ICH. the main analytical tool used was the inverse variance weighting (IVW) method, supplemented by sensitivity analyses. The findings suggest a positive association between AD and ICH. The level of genetic prediction of AD was consistently positively correlated with ICH, as evidenced by IVW (odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.33E\u0026thinsp;+\u0026thinsp;12; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These associations were further supported by the results of all sensitivity analyses. In addition, Illnesses of mother: Alzheimer's disease/dementia was associated with ICH (IVW: OR\u0026thinsp;=\u0026thinsp;5.947; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). AD and ICH have a unidirectional causal relationship, and illnesses of mother is a major factor with ICH, whereas Alzheimer's disease or a family history of Alzheimer's disease is not.\u003c/p\u003e","manuscriptTitle":"Causal Relationship between Alzheimer's disease and Intracerebral hemorrhage: a two-way Mendelian randomization analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-22 17:58:51","doi":"10.21203/rs.3.rs-3936524/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"840f695a-22ee-412d-b010-447921208788","owner":[],"postedDate":"February 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-24T12:23:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-22 17:58:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3936524","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3936524","identity":"rs-3936524","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}