Linking Parkinson’s disease and Alzheimer’s disease: A Mendelian Randomization Study on Causal Relationships and Clinical Implications

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Contemporary research has unraveled distinct key mechanisms that account for two diseases, although disturbance of pathways that responsible for Parkinson's Disease has been found to be implicated in Alzheimer's Disease, and vice versa. In order to investigate if there is causal relationship between these two intertwined diseases, we conducted a two-sample Mendelian randomisation analysis encompassing 482,730 PD individuals and 218,792 AD individuals. Using SNPs from publicly available genome-wide association study datasets, we chose instrumental variables for a two-sample MR analysis while adhering to important Mendelian randomisation assumptions. The primary analysis using the Inverse Variance Weighted method indicated a significant causal effect of Parkinson's Disease on Alzheimer's Disease (OR = 1.071766, 95% CI: 1.0062448, 1.141554; p = 0.03218492). The IVW analysis tested the bi-directional relationship between Alzheimer's disease (finn-b-G6_ALZHEIMER) and Parkinson's disease (ieu-b-7), with no significant evidence suggesting reverse causation. Sensitivity analyses, including heterogeneity tests, MR-Egger regression, leave-one-out, and reverse causation analysis, validated the robustness of the findings, showing no evidence of pleiotropy or. Clinically, these data suggest that Parkinson's Disease patients may benefit from early Alzheimer's Disease screening and interventions, which could improve individualized treatment approaches and public health initiatives. This study underlines the ability of genetic insights to influence integrated care approaches, ultimately improving patient outcomes with neurodegenerative diseases. Parkinson's disease Alzheimer's disease Mendelian randomization genetic epidemiology neurodegenerative disorders causal Inference GWAS public health Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Parkinson's disease (PD), known for its symptoms of rest tremor, bradykinesia, rigidity, and postural instability, is among the most prevalent neurodegenerative disorders. The disease significantly impacts patients' quality of life and increases the burden on both patients and their caregivers due to its motor and non-motor symptoms. Genetics (such as mutations in the SNCA, LRRK2, and PARK gene) and environmental (such as pesticides, herbicides, and heavy metals) factors, and aging are the underlying driving force of PD. At molecular level, pathogenic α-synuclein (α-syn)-induced various cascades of detrimental effects on cellular organelles, including mitochondria, lysosome, have been recognized as primary culprit to propel the gradual degeneration of dopaminergic neurons in the substantia nigra in the development of PD 1 . Alzheimer's disease (AD) is also a progressive, irreversible brain disorder associated with aging. It is characterised by significant memory loss, confusion, and personality changes. AD is primarily driven by aging, with Apolipoprotein E ɛ4 (APOE4) being a significant hereditary risk factor. Pathological pathways include extracellular amyloid-beta (Aβ) plaque buildup and formation of tau hyperphosphorylation-related neurofibrillary tangles. These pathological changes trigger neuroinflammation, synaptic malfunction, and mitochondrial damage, eventually leading to neurodegeneration and cognitive loss in AD 2 , 3 . The two most prevalent neurodegenerative diseases, PD and AD, have a critical impact on patient health, quality of life and strain public healthcare systems across the globe. Despite their diverse clinical presentations, there is rising consensus among the scientific community based on increasing evidence of shared pathogenesis. For instance, genetic variations such as shared genetic loci (i.e. near LCORL, CLU, SETD1A/KAT8) 4 in both diseases have been identified to play influential roles in various cellular processes including protein uptake, lipid transport, transcriptional regulation, and lysosomal/autophagic dysfunction, which are central to the progression of both diseases. Neuroinflammation, a typically protective response in neural cells, is chronically activated and with an imbalanced redox state leading to the characteristic oxidative stress and cellular damage in these two diseases, positioning both PD and AD as brain inflammatory diseases. In response to oxidative stress, activated microglia act to release various inflammatory mediators including Cyclooxygenase-2(COX-2)/prostaglandins (PGs), iNOS/nitric oxide (NO), and/or cytokines that contribute to pathogenesis of both AD 5 6 and PD 7 8 . PD and AD are both characterized by the aggregation of misfolded proteins, with AD characterized by their hallmark tau tangles and amyloid plaques, whereas Lewy bodies primarily composed of α-syn acting as hallmark features of PD. There is emerging evidence of cross-talk and mutual exacerbation between these proteinopathies. For instance, tau and α-syn may promote mutual fibrillization and solubility 9 , 10 . In addition, amyloid beta increased aggregation propensity on α-syn 11 . Despite this mounting evidence, any firm directionally between PD and AD has yet to be conclusively elucidated. Understanding these connections would potentially foster more effective diagnostic and treatment plans 12 Using genetic variants as instrumental variables (IVs) to infer causality, Mendelian randomization (MR) will be used in this study to examine the causative link between PD and AD 13 . The study seeks to provide insights that could transform clinical practices in favor of better patient outcomes. Methods Study Design This study assessed the causal relationship between sporadic PD and AD using the well-described two-sample MR analysis method. The Instrumental Variables (IVs) in this analysis were SNPs extracted from publicly available IEU GWAS database 14 containing studies that were endorsed by ethical standards committees with respect to human clinical research. Additionally, this study relied on the three key assumptions of MR: (a) The IVs are associated with the exposure; (b) The IVs are not associated with underlying confounders; (c) The IVs do not cause the outcome in the absence of the exposure 15 . The IVs extracted from the exposure and outcome datasets were subsequently linkage disequilibrium (LD) clumped at r² = 0.1. Lastly, to validate that the IVs chosen adhered to the established assumptions, a series of sensitivity analyses were also performed, and a p-value ≤ 0.05 was considered significant. GWAS dataset PD GWAS dataset The GWAS summary dataset for PD study, ieu-b-7, originally made public in the International Parkinson’s Disease Consortium was extracted from the IEU GWAS database. The dataset assessed 482,730 individuals of European descent (Ncase = 33,674 and Ncontrol = 449,056), with 17,891,936 SNPs identified. AD GWAS dataset The GWAS summary dataset for the AD studies, finn-b-G6_ALZHEIMER, were obtained from the IEU GWAS database. The dataset assessed individuals of European descent, with finn-b-G6_ALZHEIMER measuring 218,792 individuals (Ncase = 3,899 and Ncontrol = 214,893). Mendelian randomisation The MR analysis was done in R studio (V.4.3.3), utilising the R package “TwoSampleMR (v6.1)”. Two MR analysis methods were employed to determine the causal relationship between PD and AD, namely the Inverse Variance Weighted (IVW) and MR-Egger. The IVW analysis was the main tool utilised to determine the causal relationship of the exposure on the outcome, and MR-Egger served as a tool for causal estimates and to further validate the IVW results 16 . The linkage disequilibrium (LD) criterion was set to be r²< 0.1, aiming to balance the necessity for independence of SNPs with their natural proximity, with these standard values reducing the danger of incorporating several SNPs that effectively capture the same genetic signal and ultimately avoiding biasing the results. 17 Sensitivity analyses Heterogeneity and Pleiotropy analysis To examine for heterogeneity in the datasets, the heterogeneity statistics function within the R package was used to generate the Cochran’s Q value for each MR analysis method. In this study, the focus was particularly on the IVW Q value, given its statistical precision and sensitivity 18 . Horizontal pleiotropy analysis was performed using MR-Egger regression 19 . The regression plot was generated and the P-value and egger intercepts were analysed for presence of pleiotropy. Additionally, A funnel plot of the IV strength, a reciprocal of the standard error of the IV estimate, against the IV estimates was generated to visualise the heterogeneity and symmetry of the instrumental variables. Leave-one-out analysis To assess the robustness of the MR analysis, a leave-one-out analysis was done by iteratively removing each outcome IV associated with AD. The remaining genetic variables were then recalculated to evaluate the association between AD and PD without the excluded IV, to identify IVs that had a significant statistical influence on the MR analysis. A summarised plot of the leave-one-out analysis was generated by the TwoSampleMR package. Bi-directional causality test A reverse causation analysis of the outcome on the exposure was performed to analyse if the outcome, AD, had a causal relationship with the exposure, PD 16 . The analysis was done by reversing the initial instruments of the exposure PD dataset (ieu-b-7) and the outcome AD dataset (finn-b-G6_ALZHEIMER). Once reversed, an MR analysis was then performed on the exposure instruments in AD dataset (finn-b-G6_ALZHEIMER) against the outcome instrument PD dataset (ieu-b-7), and the IVW was analysed for bi-directionality. Results Causal Association of PD on AD MR analysis of the harmonised datasets revealed a causal association between sporadic PD (ieu-b-7) and AD (finn-b-G6_ALZHEIMER), IVW (OR: 1.072, 95% CI: 1.006–1.142, p = 0.00789) and MR-Egger (OR: 1.171,95% CI: 1.028–1.335, p = 0.0245). Sensitivity analyses to increase confidence Heterogeneity test performed were insignificant (p > 0.05) as they returned results that indicated homogeneity in instruments ( Q = 29.0312, p = 0.5676083). Tests for directional pleiotropy also showed no evidence of pleiotropy with Egger intercept close to zero (egger intercept = -1.8e-02, SE = 1.2e-02, p > 0.05). Funnel plot diagram depicted a symmetrical distribution of the IVs, with the data points also assuming a funnel shape (Fig. 1 ). Additionally, the leave-one-out analysis performed on the harmonised data revealed that all the points were within the 95% CI range and were away from zero (Fig. 2 ). Directionality test Reversing the exposure and outcome instruments to test for bi-directionality revealed that the causal association of PD and AD was unidirectional, IVW (p = 0.468), with no significant (p > 0.05) evidence implying that AD has a causal association with PD. These results indicate that there is no significant evidence supporting reverse causation, where Alzheimer's disease causes Parkinson's disease. Furthermore, tests for horizontal pleiotropy and heterogeneity were also statistically insignificant (p > 0.05). Replication with a separate dataset The replication of the MR analysis with a second AD outcome, ebi-a-GCST90027158 dataset from a separate database, showed a similar causal association of PD on AD. IVW (OR = 1.0889, 95% CI: 1.0362–1.1442; p = 0.0007652). Heterogeneity tests of the initial IVs from the harmonised dataset showed homogeneity of the in the IVs via IVW ( Q = 28.3, p = 0.607) and MR Egger ( Q = 27.8, p = 0.579). There was no evidence of pleiotropy (egger intercept = 3.27e-05, SE = 4.97e-05, p = 0.516), along with symmetrical distributions in the funnel plot (Fig. 3 ) and leave-one-out analysis again revealed that all the points were within the 95% CI range and were away from zero (Fig. 4 ). Discussion We conducted an MR analysis to investigate the relationship between sporadic PD and AD. The primary analysis, using the Inverse Variance Weighted (IVW) approach, revealed a nominally significant association IVW(OR: 1.072, 95% CI: 1.006–1.142, p = 0.00789) and MR Egger (OR: 1.171,95% CI: 1.028–1.335, p = 0.0245), with the robustness of these results further confirmed via sensitivity, overall supporting the idea of a causational link between these two common neurodegenerative illnesses. Additional directional tests showed the absence of any bidirectional relationship between sporadic PD and AD, therefore enforcing our findings that sporadic PD had a unidirectional causational effect on AD. The validity of the results we gathered from our study are based on 3 broad assumptions: (a) The IVs are associated with the exposure; (b) the IVs are not associated with underlying confounders and (c) the IVs do not cause the outcome in the absence of the exposure. To ensure that our IVs are associated with PD, we filtered for SNPS set to a p-value of 5×10 − 8 to ensure that our chosen IVs were highly significant and therefore closely associated to the exposure. In order to ensure that the IVs were not associated with any confounders, we first extracted our datasets from the same geographical population (European descent), and ensured the datasets passed a series of sensitivity analyses assessing homogeneity (Cochran’s Q value), pleiotropy (MR-Egger regression) and impacts of single SNPs (leave-one-out analysis). Additionally, through the use of MR Egger regression, we looked out for the presence of any horizontal pleiotropy, finding no evidence of pleitrophy as well as Egger intercept being encouragingly close to zero, altogether suggesting that our IVs did not influence the outcome in the absence of the exposure. Our study suggests the presence of a causal relationship between sporadic PD and AD. Such findings are in line with recent literature, in which there is increasing evidence of an association between the two neurodegenerative diseases. Hulya et. al. in 2002 substantiated the correlation between PD and AD, utilizing modern immunohistochemical techniques, in particular α-syn immunohistochemical analysis to identify a neuropathologic basis for dementia in PD patients. The presence of both Lewy body pathology and Alzheimer-type alterations in these patients suggests a potential interplay between the two illnesses 20 . In the context of MR analysis, these results stand in contrast to a previous MR analysis which failed to identify any significant correlation between PD and AD. It should be noted that this previous 2018 study utilized a comparatively smaller AD GWAS datasets (74,046 individuals) in comparison to our 218,792 individuals which likely limited the lead SNPs detected in the former 21 . Other forms of clinical data analysis and bench work have reported similar correlations as well as possible mechanisms for such associations. For instance, Dugger BN et. al. 22 noted that a substantial proportion of PD cases (> 30%) have comorbid AD pathological changes, such as β-amyloid deposits (neuritic and diffuse plaques) as well as tau-positive neurofibrillary tangles. In another study, seed amplification assay was used to analyse cerebrospinal fluid samples, revealing that 23% had Lewy-Body (LB) pathology in patients with mild cognitive impairment or dementia 23 , therefore suggesting a link between the two major neurodegenerative disorders. Our findings, which suggest the causational relationship of sporadic PD on AD rather than a bidirectional one, are consistent with existing findings, Amyloid-beta, a major driving factor in the pathophysiology of AD, has been found to be associated with neuropsychiatric symptoms and cognitive decline in PD, but not its hallmark motor symptoms 24 . Parallel to this was a study by Jo et al on another major driving factor in AD, APOE (particularly APOE4) was associated with a similar cognitive trajectory decline and higher frequency of neuropsychiatric symptoms in PD patients, with the study noting that no APOE genotype was associated with motor progression 25 . This therefore supports our conclusion that it is unlikely for AD to be causational on PD. Since Ueda et al . (1993) first postulated α-syn's role in amyloidosis pathways in AD 26 , and later investigations have showed higher levels of α-syn in CSF of AD patients, highlighting its potential as a biomarker 27 . Differential diagnosis appears to indicate that α-syn aggregation in brains may contribute to cognitive impairment in AD patients, probably via its interaction with amyloid-beta and tau proteins 28 , 29 . Daniel Twohug et al reported the presence of α-SN pathology in more than half of the autopsied AD brains as well as raised α-SN levels in CSF in patients with mild cognitive impairment and AD 30 . This could be so as α-SN is a precursor protein to the non-amyloid component of senile plaques, which is a pathological feature of AD brains. Thus, α-syn-related pathological changes may underlie the causational effect of PD on AD. Such findings have huge implications both clinically and on public health policies. By revealing a significant genetic link between Parkinson's Disease (PD) and Alzheimer's Disease (AD), we advocate for early AD screening in PD patients. Early detection and intervention can delay AD onset, enhance patient outcomes, and reduce disease burden. Given that AD and PD are the most common two neurodegenerative diseases, clinicians can therefore consider assessing patients diagnosed with PD for any features of AD or assess for any signs and symptoms of AD in subsequent follow-ups, especially since both are chronic and long-term diseases requiring long term care. Although Parkinson's disease dementia (PDD) and AD both involve cognitive decline 31 , the progression and impact of these two diseases still differ. This distinction has long been established in the longitudinal development of clinical features, with PDD distinguishing itself in the development of broader set of neurodegenerative processes including cognitive fluctuations, visual and auditory hallucinations, depression, falls and sleeping difficulties which set it apart, whereas AD is predominantly defined by memory impairment as the primary clinical diagnostic criteria rather than associating with motor symptoms 32 , 33 . Moreover, in the long run, care for such chronic diseases can be expensive both from a public health standpoint and for patients. Hence, early detection of AD allows for early intervention and in turn higher levels of success of treatments, especially since it remains a fact that there is a lack of long-lasting, efficacious treatment options for AD. In fact, with evidence of increased risks of AD that are associated with PD and the known fact that neurodegenerative changes occur long before symptom onset, clinicians can manage patients preventatively by identifying AD in PD patients and administrating them with drugs that can help delay the onset of AD, such as lecanemab, a humanized monoclonal antibody that targets soluble amyloid-beta protofibrils and has been recently approved by Food and Drug Administration (FDA), 34 , therefore reducing the burden of disease. In a broader sense, proving the causal relationship between PD and AD may have an impact on public health policies as it justifies funding for neurodegenerative integrated care programs. Our findings aim to advance personalized treatment and patient-centered care strategies, with the hope of improving the management of neurodegenerative diseases in line with their genetic nature. Given not only the shared clinical and pathological features, but the causal link between the two most prevalent neurodegenerative diseases, it encourages the need for a multidisciplinary strategy that accounts for the interconnectedness of these illnesses when caring for such patients. While the sensitivity analyses undertaken in this work addressed various potential causes of bias, any constraints should be considered. Our study only included subjects of European ancestry. Genetic differences and disease prevalence can vary greatly amongst populations, and despite the benefit provided by the robust sample size of European cohort to minimize the bias caused by population stratification, the causative routes between PD and AD may not be the same in non-European cultures. This calls for future studies to replicate these methods to a broader global context covering databases of other ethnicities. Abbreviations PD Parkinson’s disease AD Alzheimer’s disease PDD Parkinson’s disease dementia SNCA Synuclein Alpha LRRK2 Leucine-rich repeat kinase 2 PARK Parkinson’s disease-associated genes α-syn Alpha-synuclein APOE4 Apolipoprotein E ɛ4 Aβ Amyloid-beta LCORL Ligand-dependent nuclear receptor corepressor-like CLU Clusterin SETD1A SET domain containing 1A KAT8 Lysine acetyltransferase 8 COX-2 Cyclooxygenase-2 PGs Prostaglandins iNOS Inducible nitric oxide synthase NO Nitric oxide IVs Instrumental variables MR Mendelian randomization IVW Inverse Variance Weighted LD Linkage disequilibrium SNPs Single nucleotide polymorphisms SE Standard error CI Confidence interval Q Cochran’s Q statistic FDA Food and Drug Administration Declarations Author information Authors and Affliations National Neuroscience Institute, Singapore General Hospital, 169856, Singapore Joshua Kuruvilla, Yuan Ting Yong, Wei Jun Lee, Kenneth Yap, Xiao Deng, Eng-King Tan, and Bin Xiao Duke-NUS Medical School, 169857, Singapore Eng-King Tan, and Bin Xiao Competing interests The authors declare no competing interests. Ethics Declaration Ethics approval and consent to participate This article includes human volunteers from numerous past research. All participants in all corresponding original research provided informed consent, as specified in the Materials and methods section. Our analysis is based on publicly available, large-scale datasets, rather than individual-level data. Thus, ethical approval was not sought. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding The authors thank Singapore Ministry of Health’s National Medical Research Council for their support. (Open Fund Large Collaborative Grant (MOH-000207) and Singapore Translational Research (STaR) Investigator Award (NMRC/STaR/0030/2018) to TEK, TA award (MOH-001272) and OF-YIRG (MOH-000660) to XB). Author Contribution Joshua Kuruvilla and Bin Xiao designed the study. Joshua Kuruvilla, Yuan Ting Yong, Wei Jun Lee, Kenneth Yap, Xiao Deng, Eng-King Tan, and Bin Xiao wrote the main manuscript text. Joshua Kuruvilla, Wei Jun Lee, and Kenneth Yap prepared the figures. All authors reviewed the manuscript Acknowledgement The authors thank Singapore Ministry of Health’s National Medical Research Council for their support. (Open Fund Large Collaborative Grant (MOH-000207) and Singapore Translational Research (STaR) Investigator Award (NMRC/STaR/0030/2018) to TEK, TA award (MOH-001272) and OF-YIRG (MOH-000660) to XB). Data Availability This research was made possible by the use of Open Access data from the UK Biobank (UKB). We would like to thank the International Parkinson’s Disease Genomics Consortium (IPDGC) (https://pdgenetics.org/) and MRC IEU OpenGWAS (https://gwas.mrcieu.ac.uk/) for this publicly available access to genome-wide association study data. We appreciate the contributions of all participants and any datasets that were mentioned. 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Supplementary Files Picture5.jpg Supplementary Figure 1: Scatter plot depicting the various MR analysis methods utilised on the SNP effect of Alzheimer's disease ( finn-b-G6_ALZHEIMER ) vs. Parkinson's disease ( ieu-b-7 ). Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5954887","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":414726694,"identity":"3bbae947-077e-4df0-bac6-61837632fc26","order_by":0,"name":"Joshua Kuruvilla","email":"","orcid":"","institution":"National Neuroscience Institute","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Kuruvilla","suffix":""},{"id":414726695,"identity":"d68501f8-6c73-4a25-b6f3-eb2b6aa8fe48","order_by":1,"name":"Yuan Ting Yong","email":"","orcid":"","institution":"National Neuroscience Institute","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"Ting","lastName":"Yong","suffix":""},{"id":414726696,"identity":"bb4b0da4-440c-4953-a18a-b0a098337a79","order_by":2,"name":"Wei Jun Lee","email":"","orcid":"","institution":"National Neuroscience Institute","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"Jun","lastName":"Lee","suffix":""},{"id":414726697,"identity":"fe070918-cf2d-41e8-a5df-bbcf5c0b97bf","order_by":3,"name":"Kenneth Yap","email":"","orcid":"","institution":"National Neuroscience Institute","correspondingAuthor":false,"prefix":"","firstName":"Kenneth","middleName":"","lastName":"Yap","suffix":""},{"id":414726699,"identity":"0c5c1309-125d-40ef-a613-090acd5ef138","order_by":4,"name":"Xiao Deng","email":"","orcid":"","institution":"National Neuroscience Institute","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Deng","suffix":""},{"id":414726700,"identity":"b59c9a23-f69a-4a1d-8b03-e8271489b356","order_by":5,"name":"Eng-King Tan","email":"","orcid":"","institution":"National Neuroscience Institute","correspondingAuthor":false,"prefix":"","firstName":"Eng-King","middleName":"","lastName":"Tan","suffix":""},{"id":414726702,"identity":"199228e1-892f-4726-90f6-4ac7b122adbd","order_by":6,"name":"Bin Xiao","email":"data:image/png;base64,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","orcid":"","institution":"National Neuroscience Institute","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Xiao","suffix":""}],"badges":[],"createdAt":"2025-02-04 04:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5954887/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5954887/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76660873,"identity":"097cfd81-e5db-4d8c-8418-ba1268c70ab0","added_by":"auto","created_at":"2025-02-19 12:02:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFunnel plot for MR analysis using the inverse variance weighted and MR-Egger methods s of ieu-b-7 (Parkinson’s Disease) on \u003c/em\u003efinn-b-G6_ALZHEIMER \u003cem\u003e(Alzheimer’s Disease).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5954887/v1/e917fa30f7f38b83a0237133.jpg"},{"id":76662842,"identity":"6378ba2d-9892-42a8-82b8-0aedb503cfb3","added_by":"auto","created_at":"2025-02-19 12:10:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85085,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLeave-one-out sensitivity analysis for MR analysis of ieu-b-7 (Parkinson’s Disease) on \u003c/em\u003efinn-b-G6_ALZHEIMER \u003cem\u003e(Alzheimer’s Disease).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5954887/v1/c62dc0c8197c630cd415e362.jpg"},{"id":76660875,"identity":"cf69d631-c202-4b92-8e0e-8432c319fd85","added_by":"auto","created_at":"2025-02-19 12:02:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35576,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFunnel plot for MR analysis using the inverse variance weighted and MR-Egger methods s of replication data ieu-b-7 (Parkinson’s Disease) on ieu-b-5067 (Alzheimer’s Disease)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5954887/v1/20d527ea8d13f468135fb910.jpg"},{"id":76660878,"identity":"43cc37a7-b86c-4da2-b244-7f87daee569a","added_by":"auto","created_at":"2025-02-19 12:02:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84367,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLeave-one-out sensitivity analysis for MR analysis of replication data ieu-b-7 (Parkinson’s Disease) on ieu-b-5067 (Alzheimer’s Disease).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5954887/v1/1277d791018ecaababa4944c.jpg"},{"id":79038834,"identity":"15c1095f-6aab-44b8-8bf4-f2d8d7625335","added_by":"auto","created_at":"2025-03-23 10:16:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":944193,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5954887/v1/42f980d4-dccf-46b4-9ec3-d9d4415dd52d.pdf"},{"id":76660874,"identity":"07aef35b-5db3-4a2d-ba2c-2b50cc7156e8","added_by":"auto","created_at":"2025-02-19 12:02:22","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSupplementary Figure 1: Scatter plot depicting the various MR analysis methods utilised on the SNP effect of Alzheimer's disease (\u003c/em\u003efinn-b-G6_ALZHEIMER\u003cem\u003e) vs. Parkinson's disease (\u003c/em\u003eieu-b-7\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5954887/v1/3241b0ad656674f962c0db1b.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Linking Parkinson’s disease and Alzheimer’s disease: A Mendelian Randomization Study on Causal Relationships and Clinical Implications","fulltext":[{"header":"Background","content":"\u003cp\u003eParkinson's disease (PD), known for its symptoms of rest tremor, bradykinesia, rigidity, and postural instability, is among the most prevalent neurodegenerative disorders. The disease significantly impacts patients' quality of life and increases the burden on both patients and their caregivers due to its motor and non-motor symptoms. Genetics (such as mutations in the SNCA, LRRK2, and PARK gene) and environmental (such as pesticides, herbicides, and heavy metals) factors, and aging are the underlying driving force of PD. At molecular level, pathogenic α-synuclein (α-syn)-induced various cascades of detrimental effects on cellular organelles, including mitochondria, lysosome, have been recognized as primary culprit to propel the gradual degeneration of dopaminergic neurons in the substantia nigra in the development of PD \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlzheimer's disease (AD) is also a progressive, irreversible brain disorder associated with aging. It is characterised by significant memory loss, confusion, and personality changes. AD is primarily driven by aging, with Apolipoprotein E ɛ4 (APOE4) being a significant hereditary risk factor. Pathological pathways include extracellular amyloid-beta (Aβ) plaque buildup and formation of tau hyperphosphorylation-related neurofibrillary tangles. These pathological changes trigger neuroinflammation, synaptic malfunction, and mitochondrial damage, eventually leading to neurodegeneration and cognitive loss in AD \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe two most prevalent neurodegenerative diseases, PD and AD, have a critical impact on patient health, quality of life and strain public healthcare systems across the globe. Despite their diverse clinical presentations, there is rising consensus among the scientific community based on increasing evidence of shared pathogenesis. For instance, genetic variations such as shared genetic loci (i.e. near LCORL, CLU, SETD1A/KAT8) \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e in both diseases have been identified to play influential roles in various cellular processes including protein uptake, lipid transport, transcriptional regulation, and lysosomal/autophagic dysfunction, which are central to the progression of both diseases. Neuroinflammation, a typically protective response in neural cells, is chronically activated and with an imbalanced redox state leading to the characteristic oxidative stress and cellular damage in these two diseases, positioning both PD and AD as brain inflammatory diseases. In response to oxidative stress, activated microglia act to release various inflammatory mediators including Cyclooxygenase-2(COX-2)/prostaglandins (PGs), iNOS/nitric oxide (NO), and/or cytokines that contribute to pathogenesis of both AD\u003csup\u003e5 6\u003c/sup\u003e and PD \u003csup\u003e7 8\u003c/sup\u003e. PD and AD are both characterized by the aggregation of misfolded proteins, with AD characterized by their hallmark tau tangles and amyloid plaques, whereas Lewy bodies primarily composed of α-syn acting as hallmark features of PD. There is emerging evidence of cross-talk and mutual exacerbation between these proteinopathies. For instance, tau and α-syn may promote mutual fibrillization and solubility \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In addition, amyloid beta increased aggregation propensity on α-syn \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite this mounting evidence, any firm directionally between PD and AD has yet to be conclusively elucidated. Understanding these connections would potentially foster more effective diagnostic and treatment plans \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eUsing genetic variants as instrumental variables (IVs) to infer causality, Mendelian randomization (MR) will be used in this study to examine the causative link between PD and AD \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe study seeks to provide insights that could transform clinical practices in favor of better patient outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis study assessed the causal relationship between sporadic PD and AD using the well-described two-sample MR analysis method. The Instrumental Variables (IVs) in this analysis were SNPs extracted from publicly available IEU GWAS database \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e containing studies that were endorsed by ethical standards committees with respect to human clinical research. Additionally, this study relied on the three key assumptions of MR: (a) The IVs are associated with the exposure; (b) The IVs are not associated with underlying confounders; (c) The IVs do not cause the outcome in the absence of the exposure \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The IVs extracted from the exposure and outcome datasets were subsequently linkage disequilibrium (LD) clumped at r\u0026sup2; = 0.1. Lastly, to validate that the IVs chosen adhered to the established assumptions, a series of sensitivity analyses were also performed, and a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGWAS dataset\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePD GWAS dataset\u003c/h2\u003e \u003cp\u003eThe GWAS summary dataset for PD study, ieu-b-7, originally made public in the International Parkinson\u0026rsquo;s Disease Consortium was extracted from the IEU GWAS database. The dataset assessed 482,730 individuals of European descent (Ncase\u0026thinsp;=\u0026thinsp;33,674 and Ncontrol\u0026thinsp;=\u0026thinsp;449,056), with 17,891,936 SNPs identified.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAD GWAS dataset\u003c/h3\u003e\n\u003cp\u003eThe GWAS summary dataset for the AD studies, finn-b-G6_ALZHEIMER, were obtained from the IEU GWAS database. The dataset assessed individuals of European descent, with finn-b-G6_ALZHEIMER measuring 218,792 individuals (Ncase\u0026thinsp;=\u0026thinsp;3,899 and Ncontrol\u0026thinsp;=\u0026thinsp;214,893).\u003c/p\u003e\n\u003ch3\u003eMendelian randomisation\u003c/h3\u003e\n\u003cp\u003eThe MR analysis was done in R studio (V.4.3.3), utilising the R package \u0026ldquo;TwoSampleMR (v6.1)\u0026rdquo;. Two MR analysis methods were employed to determine the causal relationship between PD and AD, namely the Inverse Variance Weighted (IVW) and MR-Egger. The IVW analysis was the main tool utilised to determine the causal relationship of the exposure on the outcome, and MR-Egger served as a tool for causal estimates and to further validate the IVW results \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The linkage disequilibrium (LD) criterion was set to be r\u0026sup2;\u0026lt; 0.1, aiming to balance the necessity for independence of SNPs with their natural proximity, with these standard values reducing the danger of incorporating several SNPs that effectively capture the same genetic signal and ultimately avoiding biasing the results. \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eHeterogeneity and Pleiotropy analysis\u003c/h2\u003e \u003cp\u003eTo examine for heterogeneity in the datasets, the heterogeneity statistics function within the R package was used to generate the Cochran\u0026rsquo;s Q value for each MR analysis method. In this study, the focus was particularly on the IVW Q value, given its statistical precision and sensitivity \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHorizontal pleiotropy analysis was performed using MR-Egger regression \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The regression plot was generated and the P-value and egger intercepts were analysed for presence of pleiotropy. Additionally, A funnel plot of the IV strength, a reciprocal of the standard error of the IV estimate, against the IV estimates was generated to visualise the heterogeneity and symmetry of the instrumental variables.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eLeave-one-out analysis\u003c/h3\u003e\n\u003cp\u003eTo assess the robustness of the MR analysis, a leave-one-out analysis was done by iteratively removing each outcome IV associated with AD. The remaining genetic variables were then recalculated to evaluate the association between AD and PD without the excluded IV, to identify IVs that had a significant statistical influence on the MR analysis. A summarised plot of the leave-one-out analysis was generated by the TwoSampleMR package.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBi-directional causality test\u003c/h2\u003e \u003cp\u003eA reverse causation analysis of the outcome on the exposure was performed to analyse if the outcome, AD, had a causal relationship with the exposure, PD \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The analysis was done by reversing the initial instruments of the exposure PD dataset (ieu-b-7) and the outcome AD dataset (finn-b-G6_ALZHEIMER). Once reversed, an MR analysis was then performed on the exposure instruments in AD dataset (finn-b-G6_ALZHEIMER) against the outcome instrument PD dataset (ieu-b-7), and the IVW was analysed for bi-directionality.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCausal Association of PD on AD\u003c/h2\u003e \u003cp\u003eMR analysis of the harmonised datasets revealed a causal association between sporadic PD (ieu-b-7) and AD (finn-b-G6_ALZHEIMER), IVW (OR: 1.072, 95% CI: 1.006\u0026ndash;1.142, p\u0026thinsp;=\u0026thinsp;0.00789) and MR-Egger (OR: 1.171,95% CI: 1.028\u0026ndash;1.335, p\u0026thinsp;=\u0026thinsp;0.0245).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses to increase confidence\u003c/h2\u003e \u003cp\u003eHeterogeneity test performed were insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) as they returned results that indicated homogeneity in instruments (\u003cem\u003eQ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29.0312, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5676083). Tests for directional pleiotropy also showed no evidence of pleiotropy with Egger intercept close to zero (egger intercept = -1.8e-02, \u003cem\u003eSE\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1.2e-02, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Funnel plot diagram depicted a symmetrical distribution of the IVs, with the data points also assuming a funnel shape (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, the leave-one-out analysis performed on the harmonised data revealed that all the points were within the 95% CI range and were away from zero (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDirectionality test\u003c/h2\u003e \u003cp\u003eReversing the exposure and outcome instruments to test for bi-directionality revealed that the causal association of PD and AD was unidirectional, IVW (p\u0026thinsp;=\u0026thinsp;0.468), with no significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) evidence implying that AD has a causal association with PD. These results indicate that there is no significant evidence supporting reverse causation, where Alzheimer's disease causes Parkinson's disease. Furthermore, tests for horizontal pleiotropy and heterogeneity were also statistically insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eReplication with a separate dataset\u003c/h2\u003e \u003cp\u003eThe replication of the MR analysis with a second AD outcome, ebi-a-GCST90027158 dataset from a separate database, showed a similar causal association of PD on AD. IVW (OR\u0026thinsp;=\u0026thinsp;1.0889, 95% CI: 1.0362\u0026ndash;1.1442; p\u0026thinsp;=\u0026thinsp;0.0007652). Heterogeneity tests of the initial IVs from the harmonised dataset showed homogeneity of the in the IVs via IVW (\u003cem\u003eQ\u0026thinsp;=\u003c/em\u003e\u0026thinsp;28.3, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.607) and MR Egger (\u003cem\u003eQ\u0026thinsp;=\u003c/em\u003e\u0026thinsp;27.8, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.579). There was no evidence of pleiotropy (egger intercept\u0026thinsp;=\u0026thinsp;3.27e-05, \u003cem\u003eSE\u0026thinsp;=\u003c/em\u003e\u0026thinsp;4.97e-05, p\u0026thinsp;=\u0026thinsp;0.516), along with symmetrical distributions in the funnel plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and leave-one-out analysis again revealed that all the points were within the 95% CI range and were away from zero (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted an MR analysis to investigate the relationship between sporadic PD and AD. The primary analysis, using the Inverse Variance Weighted (IVW) approach, revealed a nominally significant association IVW(OR: 1.072, 95% CI: 1.006\u0026ndash;1.142, p\u0026thinsp;=\u0026thinsp;0.00789) and MR Egger (OR: 1.171,95% CI: 1.028\u0026ndash;1.335, p\u0026thinsp;=\u0026thinsp;0.0245), with the robustness of these results further confirmed via sensitivity, overall supporting the idea of a causational link between these two common neurodegenerative illnesses. Additional directional tests showed the absence of any bidirectional relationship between sporadic PD and AD, therefore enforcing our findings that sporadic PD had a unidirectional causational effect on AD.\u003c/p\u003e \u003cp\u003eThe validity of the results we gathered from our study are based on 3 broad assumptions: (a) The IVs are associated with the exposure; (b) the IVs are not associated with underlying confounders and (c) the IVs do not cause the outcome in the absence of the exposure.\u003c/p\u003e \u003cp\u003eTo ensure that our IVs are associated with PD, we filtered for SNPS set to a p-value of 5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e to ensure that our chosen IVs were highly significant and therefore closely associated to the exposure. In order to ensure that the IVs were not associated with any confounders, we first extracted our datasets from the same geographical population (European descent), and ensured the datasets passed a series of sensitivity analyses assessing homogeneity (Cochran\u0026rsquo;s Q value), pleiotropy (MR-Egger regression) and impacts of single SNPs (leave-one-out analysis). Additionally, through the use of MR Egger regression, we looked out for the presence of any horizontal pleiotropy, finding no evidence of pleitrophy as well as Egger intercept being encouragingly close to zero, altogether suggesting that our IVs did not influence the outcome in the absence of the exposure.\u003c/p\u003e \u003cp\u003eOur study suggests the presence of a causal relationship between sporadic PD and AD. Such findings are in line with recent literature, in which there is increasing evidence of an association between the two neurodegenerative diseases. Hulya et. al. in 2002 substantiated the correlation between PD and AD, utilizing modern immunohistochemical techniques, in particular α-syn immunohistochemical analysis to identify a neuropathologic basis for dementia in PD patients. The presence of both Lewy body pathology and Alzheimer-type alterations in these patients suggests a potential interplay between the two illnesses \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the context of MR analysis, these results stand in contrast to a previous MR analysis which failed to identify any significant correlation between PD and AD. It should be noted that this previous 2018 study utilized a comparatively smaller AD GWAS datasets (74,046 individuals) in comparison to our 218,792 individuals which likely limited the lead SNPs detected in the former \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOther forms of clinical data analysis and bench work have reported similar correlations as well as possible mechanisms for such associations. For instance, Dugger BN et. al. \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e noted that a substantial proportion of PD cases (\u0026gt;\u0026thinsp;30%) have comorbid AD pathological changes, such as β-amyloid deposits (neuritic and diffuse plaques) as well as tau-positive neurofibrillary tangles. In another study, seed amplification assay was used to analyse cerebrospinal fluid samples, revealing that 23% had Lewy-Body (LB) pathology in patients with mild cognitive impairment or dementia \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, therefore suggesting a link between the two major neurodegenerative disorders.\u003c/p\u003e \u003cp\u003eOur findings, which suggest the causational relationship of sporadic PD on AD rather than a bidirectional one, are consistent with existing findings, Amyloid-beta, a major driving factor in the pathophysiology of AD, has been found to be associated with neuropsychiatric symptoms and cognitive decline in PD, but not its hallmark motor symptoms \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Parallel to this was a study by Jo \u003cem\u003eet al\u003c/em\u003e on another major driving factor in AD, APOE (particularly APOE4) was associated with a similar cognitive trajectory decline and higher frequency of neuropsychiatric symptoms in PD patients, with the study noting that no APOE genotype was associated with motor progression \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. This therefore supports our conclusion that it is unlikely for AD to be causational on PD.\u003c/p\u003e \u003cp\u003eSince Ueda \u003cem\u003eet al\u003c/em\u003e. (1993) first postulated α-syn's role in amyloidosis pathways in AD \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and later investigations have showed higher levels of α-syn in CSF of AD patients, highlighting its potential as a biomarker \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Differential diagnosis appears to indicate that α-syn aggregation in brains may contribute to cognitive impairment in AD patients, probably via its interaction with amyloid-beta and tau proteins \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Daniel Twohug \u003cem\u003eet al\u003c/em\u003e reported the presence of α-SN pathology in more than half of the autopsied AD brains as well as raised α-SN levels in CSF in patients with mild cognitive impairment and AD \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This could be so as α-SN is a precursor protein to the non-amyloid component of senile plaques, which is a pathological feature of AD brains. Thus, α-syn-related pathological changes may underlie the causational effect of PD on AD.\u003c/p\u003e \u003cp\u003eSuch findings have huge implications both clinically and on public health policies. By revealing a significant genetic link between Parkinson's Disease (PD) and Alzheimer's Disease (AD), we advocate for early AD screening in PD patients. Early detection and intervention can delay AD onset, enhance patient outcomes, and reduce disease burden. Given that AD and PD are the most common two neurodegenerative diseases, \u003cb\u003eclinicians\u003c/b\u003e can therefore consider \u003cb\u003eassessing\u003c/b\u003e patients diagnosed with PD for any features of AD or assess for any signs and symptoms of AD in subsequent follow-ups, especially since both are chronic and long-term diseases requiring long term care. Although Parkinson's disease dementia (PDD) and AD both involve cognitive decline \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, the progression and impact of these two diseases still differ. This distinction has long been established in the longitudinal development of clinical features, with PDD distinguishing itself in the development of broader set of neurodegenerative processes including cognitive fluctuations, visual and auditory hallucinations, depression, falls and sleeping difficulties which set it apart, whereas AD is predominantly defined by memory impairment as the primary clinical diagnostic criteria rather than associating with motor symptoms \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMoreover, in the long run, care for such chronic diseases can be expensive both from a public health standpoint and for patients. Hence, early detection of AD allows for early intervention and in turn higher levels of success of treatments, especially since it remains a fact that there is a lack of long-lasting, efficacious treatment options for AD. In fact, with evidence of increased risks of AD that are associated with PD and the known fact that neurodegenerative changes occur long before symptom onset, clinicians can manage patients preventatively by identifying AD in PD patients and administrating them with drugs that can help delay the onset of AD, such as lecanemab, a humanized monoclonal antibody that targets soluble amyloid-beta protofibrils and has been recently approved by Food and Drug Administration (FDA), \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, therefore reducing the burden of disease.\u003c/p\u003e \u003cp\u003eIn a broader sense, proving the causal relationship between PD and AD may have an impact on public health policies as it justifies funding for neurodegenerative integrated care programs. Our findings aim to advance personalized treatment and patient-centered care strategies, with the hope of improving the management of neurodegenerative diseases in line with their genetic nature. Given not only the shared clinical and pathological features, but the causal link between the two most prevalent neurodegenerative diseases, it encourages the need for a multidisciplinary strategy that accounts for the interconnectedness of these illnesses when caring for such patients.\u003c/p\u003e \u003cp\u003eWhile the sensitivity analyses undertaken in this work addressed various potential causes of bias, any constraints should be considered. Our study only included subjects of European ancestry. Genetic differences and disease prevalence can vary greatly amongst populations, and despite the benefit provided by the robust sample size of European cohort to minimize the bias caused by population stratification, the causative routes between PD and AD may not be the same in non-European cultures. This calls for future studies to replicate these methods to a broader global context covering databases of other ethnicities.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParkinson\u0026rsquo;s disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlzheimer\u0026rsquo;s disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePDD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParkinson\u0026rsquo;s disease dementia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSNCA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSynuclein Alpha\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLRRK2\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLeucine-rich repeat kinase 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePARK\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParkinson\u0026rsquo;s disease-associated genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eα-syn\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlpha-synuclein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAPOE4\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eApolipoprotein E ɛ4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAβ\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmyloid-beta\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLCORL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLigand-dependent nuclear receptor corepressor-like\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCLU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClusterin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSETD1A\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSET domain containing 1A\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eKAT8\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLysine acetyltransferase 8\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCOX-2\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCyclooxygenase-2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePGs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProstaglandins\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eiNOS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInducible nitric oxide synthase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNitric oxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIVs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstrumental variables\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIVW\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInverse Variance Weighted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinkage disequilibrium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSNPs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle nucleotide polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eQ\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCochran\u0026rsquo;s Q statistic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFDA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFood and Drug Administration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003e \u003cb\u003eAuthor information\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eAuthors and Affliations\u003c/strong\u003e \u003cp\u003eNational Neuroscience Institute, Singapore General Hospital, 169856, Singapore\u003c/p\u003e \u003cp\u003eJoshua Kuruvilla, Yuan Ting Yong, Wei Jun Lee, Kenneth Yap, Xiao Deng, Eng-King Tan, and Bin Xiao\u003c/p\u003e \u003cp\u003eDuke-NUS Medical School, 169857, Singapore\u003c/p\u003e \u003cp\u003eEng-King Tan, and Bin Xiao\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003e \u003cb\u003eEthics Declaration\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis article includes human volunteers from numerous past research. All participants in all corresponding original research provided informed consent, as specified in the Materials and methods section. Our analysis is based on publicly available, large-scale datasets, rather than individual-level data. Thus, ethical approval was not sought.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors thank Singapore Ministry of Health\u0026rsquo;s National Medical Research Council for their support. (Open Fund Large Collaborative Grant (MOH-000207) and Singapore Translational Research (STaR) Investigator Award (NMRC/STaR/0030/2018) to TEK, TA award (MOH-001272) and OF-YIRG (MOH-000660) to XB).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJoshua Kuruvilla and Bin Xiao designed the study. Joshua Kuruvilla, Yuan Ting Yong, Wei Jun Lee, Kenneth Yap, Xiao Deng, Eng-King Tan, and Bin Xiao wrote the main manuscript text. Joshua Kuruvilla, Wei Jun Lee, and Kenneth Yap prepared the figures. All authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank Singapore Ministry of Health\u0026rsquo;s National Medical Research Council for their support. (Open Fund Large Collaborative Grant (MOH-000207) and Singapore Translational Research (STaR) Investigator Award (NMRC/STaR/0030/2018) to TEK, TA award (MOH-001272) and OF-YIRG (MOH-000660) to XB).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis research was made possible by the use of Open Access data from the UK Biobank (UKB). We would like to thank the International Parkinson\u0026rsquo;s Disease Genomics Consortium (IPDGC) (https://pdgenetics.org/) and MRC IEU OpenGWAS (https://gwas.mrcieu.ac.uk/) for this publicly available access to genome-wide association study data. We appreciate the contributions of all participants and any datasets that were mentioned.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJankovic J, Tan EK. Parkinson\u0026rsquo;s disease: etiopathogenesis and treatment. 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Jan 2023;5(1):9\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa2212948\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa2212948\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"Parkinson's disease, Alzheimer's disease, Mendelian randomization, genetic epidemiology, neurodegenerative disorders, causal Inference, GWAS, public health","lastPublishedDoi":"10.21203/rs.3.rs-5954887/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5954887/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParkinson's Disease and Alzheimer's Disease are the two most common neurodegenerative conditions. Contemporary research has unraveled distinct key mechanisms that account for two diseases, although disturbance of pathways that responsible for Parkinson's Disease has been found to be implicated in Alzheimer's Disease, and vice versa. In order to investigate if there is causal relationship between these two intertwined diseases, we conducted a two-sample Mendelian randomisation analysis encompassing 482,730 PD individuals and 218,792 AD individuals. Using SNPs from publicly available genome-wide association study datasets, we chose instrumental variables for a two-sample MR analysis while adhering to important Mendelian randomisation assumptions. The primary analysis using the Inverse Variance Weighted method indicated a significant causal effect of Parkinson's Disease on Alzheimer's Disease (OR\u0026thinsp;=\u0026thinsp;1.071766, 95% CI: 1.0062448, 1.141554; p\u0026thinsp;=\u0026thinsp;0.03218492). The IVW analysis tested the bi-directional relationship between Alzheimer's disease (finn-b-G6_ALZHEIMER) and Parkinson's disease (ieu-b-7), with no significant evidence suggesting reverse causation. Sensitivity analyses, including heterogeneity tests, MR-Egger regression, leave-one-out, and reverse causation analysis, validated the robustness of the findings, showing no evidence of pleiotropy or. Clinically, these data suggest that Parkinson's Disease patients may benefit from early Alzheimer's Disease screening and interventions, which could improve individualized treatment approaches and public health initiatives. This study underlines the ability of genetic insights to influence integrated care approaches, ultimately improving patient outcomes with neurodegenerative diseases.\u003c/p\u003e","manuscriptTitle":"Linking Parkinson’s disease and Alzheimer’s disease: A Mendelian Randomization Study on Causal Relationships and Clinical Implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-19 12:02:17","doi":"10.21203/rs.3.rs-5954887/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":"5a5895ac-84cc-45dd-9b9e-7d0e802ad2d5","owner":[],"postedDate":"February 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-23T10:08:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-19 12:02:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5954887","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5954887","identity":"rs-5954887","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}