A Mendelian Randomization Study on Association of Alpha-synuclein and GPNMB with Parkinson’s Disease Risk and Progression

A Mendelian Randomization Study on Association of Alpha-synuclein and GPNMB with Parkinson’s Disease Risk and Progression | 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 A Mendelian Randomization Study on Association of Alpha-synuclein and GPNMB with Parkinson’s Disease Risk and Progression Jifeng Guo, Lizhi Li, Zhenhua Liu, Qian Xu, Xinxiang Yan, Beisha Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4525984/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Apr, 2025 Read the published version in Molecular Neurobiology → Version 1 posted 13 You are reading this latest preprint version Abstract The prevalence of Parkinson’s disease (PD) is increasing because of the aging population. Early diagnosis and prognosis of PD remain challenging, suggesting that seeking appropriate biomarkers for PD is crucial. GPNMB and Alpha-synuclein (α-syn) have been reported to contribute to PD pathogenesis and are correlated with PD onset and disease progression. We utilized Mendelian Randomization (MR) analysis to elucidate the association of GPNMB and α-syn with PD and its disease progression. Five MR methods were employed, and inverse variance weighted was chosen as the primary method. The results of MR analysis showed that cerebrospinal fluid (CSF) α-syn correlated with the Unified Parkinson's Disease Rating Scale Ⅲ (UPDRS Ⅲ) and Hoehn and Yahr (H&Y) stage, and plasma α-syn was associated with H&Y stage at baseline suggestively, indicating that α-syn is a promising biomarker for motor symptoms of PD. Overall, CSF and plasma α-syn are potential biomarkers for predicting PD motor symptoms, which warrant further studies. However, no association was detected between GPNMB and PD risk or disease progression. Parkinson’s disease Disease progression Alpha-synuclein GPNMB mendelian randomization FinnGen Figures Figure 1 Figure 2 Figure 3 Introduction Parkinson’s disease (PD) is one of the most common neurodegenerative diseases, with increasing incidence in the elderly[ 1 ]. A growing prevalence of PD is expected owing to aging populations worldwide, bringing severe societal, public, and personal burdens[ 2 ]. PD manifests four cardinal motor symptoms (rest tremor, rigidity, bradykinesia and postural instability) and other non-motor symptoms such as cognitive impairment, depression, hyposmia, and sleep disturbance[ 3 ]. Various standard rating scales can assess motor and non-motor symptoms to understand PD patients’ overall status better. Although the etiology of PD remains incompletely understood, the death of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the presence of Lewy body (LB) are primary pathological markers of PD, and LB is mainly composed of abnormally aggregated alpha-synuclein (α-syn)[ 4 , 5 ]. Given the core role of α-syn in PD, cerebrospinal fluid (CSF) and plasma α-syn of PD patients have been measured over the past decades, providing a window for early diagnosis and monitoring disease progression of PD[ 6 , 7 ]. Nevertheless, controversial results were observed due to various sample sizes, clinical heterogeneity and different measurement methods[ 6 – 8 ]. The triggering and driving event of α-syn misfolding and aggregation are poorly understood, with potential contributors including pesticides, organic solvents and metal irons[ 9 , 10 ]. Glycoprotein Nonmetastatic Melanoma Protein B protein (GPNMB), an endogenous glycoprotein involved in neuroinflammation, has been shown to interact with α-syn and facilitate its robust internalization and aggregation[ 11 , 12 ]. The GPNMB gene was first reported to be associated with PD in a genome-wide association study (GWAS) in 2013[ 13 ], supported by the following research[ 14 , 15 ]. Moreover, GPNMB is capable of attenuating astrocyte inflammation selectively and is highly expressed in SN of individuals with PD[ 16 , 17 ], and GPNMB levels in CSF and plasma correlate with PD and disease severity[ 11 , 18 ]. Though the above studies back GNPMB contributing to PD, contrary research results demonstrated no significant differences in CSF GPNMB between PD and healthy individuals[ 19 ]. Overall, observational studies of the level of α-syn and GPNMB on PD are inconsistent. Therefore, the study aims to elucidate the relationship of α-syn and GPNMB to PD and disease progression. Mendelian Randomization (MR) analysis employs the association of single nucleotide polymorphisms (SNP) and phenotypes to replace directly using data of exposure and outcome in randomized controlled trials. Based on the random allocation principle during allele allocation, MR effectively eliminates the bias of confounding factors, which is easier to carry out than RCTs. So, MR is an appropriate method to clarify the causal relationship between exposure and outcome. Given the convenience and effectiveness of MR, we performed MR analysis to determine the effects of GPNMB and α-syn on PD and its disease progression. Methods Study Design and Data Sources Instrumental variables (IV) for MR analysis must conform to the following three core assumptions: (1) IVs must be strongly correlated with exposure factors; (2) IVs should not be associated with confounding factors; (3) IVs are associated with the outcome only through exposure[ 20 ]. In the first stage of the study, we applied two-sample MR analysis to evaluate the causal association of α-syn and GPNMB with PD, and we then performed reverse MR analyses to verify the effect of PD on α-syn and GPNMB. The specific framework for the stage is illustrated in Fig. 1 . Next, two-sample MR analysis was employed to elucidate the influence of α-syn and GPNMB on PD age at onset (AAO) and disease progression. All summary statistics were obtained from previously published genome-wide association studies (GWAS), detailed in Table 1 . GWAS data for α-syn and GPNMB were derived from three GWAS, named cohort 1, cohort 2, and cohort 3, respectively in the following content. Cohort1 used an aptamer-based platform to measure the levels of α-syn, GPNMB in CSF (n = 835), plasma (n = 529), and brain (n = 380) samples from individuals with Alzheimer's disease (AD) and cognitively normal controls, and all participants in the GWAS were of European ancestry[ 21 ]. Cohort 2 measured plasma α-syn and GPNMB levels in 35287 Icelanders[ 22 ], and cohort 3 measured plasma levels of both proteins in 3301 healthy participants of European ancestry[ 23 ]. GWAS summary statistics for PD were sourced from a meta-analysis of 17 datasets from European ancestry samples conducted by Nalls et al., and the statistics applied in the study excluded data from Nalls et al. 2014, 23andMe post-Chang et al. 2017 and Web-Based Study of Parkinson’s Disease (PDWBS)[ 24 ]. Additionally, we incorporated PD GWAS data from the FinnGen Consortium as another PD outcome for MR analysis[ 25 ]. The GWAS of PD AAO was obtained from International Parkinson’s Disease Genomics Consortium (iPDGC). The GWAS of PD disease progression included 12 longitudinal cohorts. All participants in these two GWAS were also of European ancestry[ 26 , 27 ]. Table 1 The specific description of GWAS Phenotype Consortium Population Sample size (Cases/controls/others) Year Journal References Brain GPNMB - Europeans 380(248/24/108) 2021 Nature neuroscience 21 CSF GPNMB - Europeans 835(217/614/4) 2021 Nature neuroscience 21 Plasma GPNMB - Europeans 529(201/612/4) 2021 Nature neuroscience 21 The INTERVAL study, Europeans 3301 healthy participants 2018 Nature 22 The Icelandic Cancer Project (52%); deCODE genetics, Reykjavík, Iceland (48%) Europeans 35287 2021 Nature genetics 23 Brain α-syn - Europeans 380(248/24/108) 2021 Nature neuroscience 21 CSF α-syn - Europeans 835(217/614/4) 2021 Nature neuroscience 21 Plasma α-syn - Europeans 529(168/357/4) 2021 Nature neuroscience 21 The INTERVAL study, Europeans 3301 healthy participants 2018 Nature 22 The Icelandic Cancer Project (52%); deCODE genetics, Reykjavík, Iceland (48%) Europeans 35287 2021 Nature genetics 23 Parkinson’s disease International Parkinson’s Disease Genomics Consortium (IPDGC) Europeans 482,730 (33,674/449,056) 2019 The Lancet. Neurology 24 FinnGen Finnish 342499(3767/338732) - - https://www.finngen.fi/en AAO of PD IPDGC European 17996 2019 Movement disorders 26 PD clinical features DATATOP; DIGPD_chip; DIGPD_chip; HBS; NET-PD_LS1; OSLO; PARKFIT; PARKWEST; PDBP; PICNICS; PPMI; PRECEPT/POSTCEPT; PROPARK European 4093 2019 Movement disorders 27 Abbreviations: CSF: Cerebrospinal fluid; AAO: Age at onset; PD: Parkinson’s disease. Selection of Genetic Instrumental Variables Genetic Instrumental Variables were extracted from selected GWAS data. Initially, we set the significance threshold at 5 × 10 − 8 , but this yielded few or no satisfactory SNPs. Therefore, we relaxed the threshold to 1 × 10 − 5 to get enough SNPs for MR analysis. Eligible SNPs were then clumped under the condition of r 2 < 0.001 within 10Mb, according to the 1000 Genomes Project linkage disequilibrium (LD) structure. Each remaining SNP was checked using PhenoScanner v2 ( http://www.phenoscanner.medschl.cam.ac.uk/ ) to exclude SNPs related to other phenotypes that could contribute to outcomes of the study ( P 10 were regarded as sufficient statistical strength for subsequent MR analysis. Mendelian Randomization Analysis Five methods of MR analysis were performed to evaluate the causative association between exposures and outcomes, including the random effects inverse variance weighted (IVW), MR-Egger, weighted median, weighted mode and simple mode. However, IVW was considered the primary method based on the assumption that all IVs used for analysis were valid, and the other four methods were adopted to validate the results of IVW. After the Bonferroni correction, we set a statistically significant P -value threshold at less than 4.630 × 10 − 4 (0.05/108), and a P -value below 0.05 was defined as suggestively significant. Furthermore, we performed MR-PRESSO to find out outliers among the IVs. Once detecting outliers, we ruled out the outliers and re-performed MR analysis to reduce the effects of horizontal pleiotropy. Besides, Cochran’s Q test, leave-one-out methods and MR Egger regression were recommended to assess heterogeneity and pleiotropy during MR analysis. Statistical analysis was achieved using R (TwoSampleMR version 0.5.6). Results SNPs and the results of MR analysis are detailed in Supplementary Tables, with F statistics of IVs greater than the threshold of 10. Association of α-syn and GPNMB with PD As shown in Supplementary Tables, no significant association were detected between the levels of α-syn and GPNMB in brain, CSF and plasma with PD under the model of IVW method (Fig. 2 ), nor under Weighted median or other MR analyses. It is well known that α-syn aggregation is a remarkable pathologic change in PD ahead of clinical characteristics[ 28 ]. Our results are controversial with previous studies, likely because the brain samples in cohort 1 were taken from the parietal lobe cortex rather than SNpc. Regarding the results of the reverse MR analysis, a suggestive significance between PD and brain α-syn was observed under IVW method ( P = 0.022, OR = 0.95, Fig. 3 ) and Weighted median method ( P = 0.043, OR = 0.94, Supplementary Tables). In addition, MR-PRESSO identified no outliers among the SNPs, with a P -value of 0.847, indicating no horizontal pleiotropy in the results. Furthermore, no heterogeneity and horizontal pleiotropy were detected by Cochran’s Q test (Q = 15.84, P Q = 0.824) and MR-Egger_intercept test (intercept = -0.009, P intercept = 0.183, Supplementary Tables). The scatter plot and the leave-one-out plot for PD and brain α-syn are displayed in Supplementary Fig. 1A and Supplementary Fig. 1B. Association of α-syn and GPNMB with PD disease progression The IVW method indicated that levels of α-syn and GPNMB in brain, CSF and plasma did not affect AAO of PD, in line with the results of MR-Egger method and Weighted median method. In terms of their effects on baseline clinical features of PD, IVW model identified a suggestive causal association of CSF α-syn with the Unified Parkinson's Disease Rating Scale Ⅲ (UPDRS Ⅲ) ( P = 0.049, OR = 0.33), Hoehn and Yahr (H&Y) stage ( P = 0.032, OR = 0.62) and daytime sleepiness ( P = 0.012, OR = 56.62). Moreover, plasma α-syn showed a suggestive association with H&Y stage ( P = 0.018, OR = 0.93). However, none of these associations remained significant after the Bonferroni correction. The results of IVW, MR-Egger and Weighted median methods indicated that higher levels of CSF α-syn were related to lower UPDRS Ⅲ scores, and no heterogeneity and horizontal pleiotropy was inferred by Cochran’s Q test (Q = 1.62, P Q = 0.805) and MR-Egger_intercept test (intercept = -0.049, P intercept = 0.460, Supplementary Tables). Additionally, higher levels of CSF α-syn were suggestively associated with lower H&Y stages under IVW model, without heterogeneity and horizontal pleiotropy. Regarding the association between CSF α-syn and daytime sleepiness, no causative relationship was inferred, as indicated by intercept = -0.132 in MR Egger analysis. The role of plasma α-syn in H&Y stage is doubted due to inconsistent results across the three cohorts, with only a suggestive association observed in cohort 3. Despite this, both IVW and MR Egger methods provided evidence for the association, and MR-PRESSO ( P = 0.350) and Cochran’s Q test (Q = 15.11, P Q = 0.235) of cohort 3 revealed no heterogeneity and horizontal pleiotropy of results. Scatter plots and leave-one-out plots of the above results were displayed in Supplementary Fig. 1. Results of IVW model for disease progression during follow-up implied that brain α-syn and plasma GPNMB of cohort 1 were associated with REM sleep behavior disorder (RBD) ( P = 0.002, OR = 0.02) and the Schwab and England Activities of Daily Living scale 70 (SEADL70) ( P = 0.044, OR = 0.01), respectively. Nevertheless, MR Egger_intercept of these associations was greater than 0.05, meaning that the causative association is untrustworthy. Discussion Our study explored the causal relationship of GPNMB and α-syn with PD and its disease progression using two-sample MR to seek promising biomarkers for early diagnosis and monitoring the disease progression of PD. Above all, we found that CSF α-syn correlated to UPDRS Ⅲ and H&Y stage, and plasma α-syn was associated with H&Y stage suggestively on baseline clinical features. Furthermore, reverse MR analysis indicated that PD mediates the formation of α-syn in the parietal lobe cortex. However, no causal association was detected between GPNMB in brain parietal lobe cortex, CSF and plasma with PD risk and disease progression, suggesting that GPNMB is not an appropriate biomarker for PD even though it drives aggregation of α-syn and PD pathology. In harmony with previous studies, our findings underscore α-syn, particularly CSF α-syn, as a considerable potential biomarker that correlates with motor symptoms of PD. Unfortunately, we failed to discover any association of GPNMB and α-syn with disease progression during follow-up. At present, the pathogenic mechanism of PD is not thoroughly investigated due to its complexity. Neurologists make a diagnosis of PD primarily based on clinical motor symptoms and physical examination. Relying on motor features for diagnosis poses a challenge for early detection of PD, as motor symptoms typically manifest only after more than half of the dopamine neurons are lost[ 1 , 29 ]. In addition, the clinical presentations of PD are tremendously heterogeneous, accompanied by significant variability in disease progression and treatment responses[ 30 ]. Therefore, identifying biomarkers capable of diagnosing PD before the onset of motor symptoms and correlating with PD subtypes is essential. Currently, observational studies focus on biomarkers in CSF and plasma for PD diagnosis and prognosis, including α-syn, neurofilament light chain (NfL), amyloid-beta 42 (Aβ42), tau, lysosomal enzymes, and so on[ 31 , 32 , 7 ]. Among them, α-syn is the most potential marker due to its significant role in PD pathogenesis. Recently, GPNMB has also been shown to be elevated in PD and reflects the clinical severity of PD[ 11 ]. α-syn is encoded by the SNCA gene, a disease-causing gene of PD, which misfolds and aggregates subsequently into toxic forms, resulting in dopaminergic neuron degradation[ 33 ]. The formation and gradual development of aggregated α-syn are involved in the onset and disease progression of PD. Therefore, α-syn levels in various biofluids have been investigated to distinguish PD from healthy controls and other neurodegenerative diseases, as well as to predict disease progression[ 34 ]. Contradictory results of α-syn in CSF and plasma have been reported. As far as plasma α-syn is concerned, most studies have illustrated elevated levels in PD patients compared to healthy controls[ 35 , 36 ], whereas decreased and similar levels of α-syn in PD were also observed[ 24 , 37 , 38 ]. Zubelzu et al. conducted a meta-analysis on plasma α-syn levels in individuals with PD, supporting the idea that plasma α-syn levels increase in the early stages of PD[ 39 ]. Conversely, a Mendelian Randomization analysis exploring the relationship between plasma α-syn and PD revealed no causative relationship, coinciding with our results[ 40 ]. Additionally, the levels of plasma α-syn are also significantly related to clinical features of PD, including lower age, shorter disease duration and milder motor impairment[ 39 ], and the results of the study also showed that higher levels of plasma α-syn were associated with a lower H&Y stage. Concerning the effect of CSF α-syn on PD, the majority of studies endorse that CSF α-syn concentrations are lower in subjects with PD compared to normal controls[ 41 , 42 ]. The relationship between CSF α-syn levels and disease severity remains elusive, but CSF α-syn levels are likely to correlate with H&Y stage and postural instability[ 43 , 44 ]. Our research suggests that increased CSF α-syn is linked with milder motor disorders and lower H&Y stages in PD patients. Still, no association between α-syn in CSF and PD was detected. In general, the correlation between α-syn in CSF and plasma and the degree of motor impairment is consistent with previous research, but regarding the association between α-syn with PD, the results demonstrated no correlation. We speculate that the inverse correlation between motor impairment in PD and α-syn in CSF and plasma is because plenty of α-syn form LBs in neurons of patients with PD, which leads to a lower level of α-syn in CSF and plasma and severe motor disorder. We presented the results that GPNMB in brain, CSF and plasma did not correlate with PD or its disease progression, incompatible with the observational study, which illustrated elevated plasma GPNMB is associated with PD and more severe motor impairment[ 11 ]. The reverse MR analysis also revealed no causal relationship between PD and GPNMB in the parietal lobe cortex, CSF and plasma. The inconsistent findings may be attributed to the following reasons. Firstly, the observational studies recruited only 731 subjects diagnosed as PD and 59 controls, whose inadequate sample size would bring inaccurate conclusions. Secondly, the detection level of GPNMB is influenced by examination methods. The observational study used Enzyme-Linked Immunosorbent Assay (ELISA) to measure the level of GPNMB, which differed from the methods of GWAS. Overall, it is noted that GPNMB interacting with α-syn plays a crucial role in the onset of PD and elevated expression of GPNMB in SN of PD patients[ 11 ]. However, using MR analysis, we found that GPNMB is unsuitable for diagnosing PD and monitoring its disease progression. Our research is an MR study to address the relationship of α-syn and GPNMB with PD, and we employed several GWAS and only reserved SNP with F > 10 to ensure that the results were robust. However, several limitations must be mentioned. First of all, the population of GWAS adopted in the study are primarily of European ancestry, meaning that findings for the study cannot be extended to other populations directly. Next, the sample size of GWAS for α-syn and GPNMB is relatively small and available IVs for several MR analyses is fewer, affecting the credibility of MR results. Lastly, our research only detects a suggestive association of α-syn with PD phenotypes, and the association did not exist after the Bonferroni correction. Therefore, more cohort and multicenter research are warranted to validate our findings. In short, the study found that CSF and plasma α-syn were related to the severity of motor impairment in PD patients suggestively, whereas there was no correlation between GPNMB and PD based on MR analysis. These results provide an insight into profoundly understanding the role of α-syn and GPNMB in the pathogenesis of PD and give evidence for the selection of PD biomarkers. However, there are some limitations in our study, and the results need more research to warrant. Statements & Declarations Declarations Acknowledgments Summary statistics of PD (iPDGC) and AAO and disease progression of PD were downloaded from iPDGC (https://pdgenetics.org/). Summary statistics of PD (FinnGen) were downloaded from FinnGen (https://www.finngen.fi/fi) and We want to acknowledge the participants and investigators of the FinnGen study. The summary GWAS data of cohort 1 could be acquired from the corresponding author. The GWAS summary statistics data of α-syn and GPNMB (cohort 2 and cohort 3)was obtained from websites (https://www.decode.com/summarydata/and http://www.phpc.cam.ac.uk/ceu/proteins/, respectively). Competing Interests The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. Ethics approval The study is a Mendelian Randomization Study, whose data were from public GWAS. Therefore, all relevant ethics statements are shown in the original GWAS, and the study did not approve an ethnical statement. Funding This study was supported by the National Key Research and Development Program of China (Grant No. 2021YFC2501204); Technology Major Project of Hunan Provincial Science and Technology Department (Grant No.2021SK1010); National Natural Science Foundation of China (Grant No.82071439, Grant No. 82271281, Grant No. 81974202,Grant No.U20A20355); Innovative team program from Department of Science & Technology of Hunan Province (Grant No.2019RS1010), and Innovation-driven Team Project from Central South University (Grant No.2020CX016). Author Contributions Lizhi Li: conception, statistical analysis and article writing. Xinxiang Yan: article writing. Qian Xu: article writing. Zhenhua Liu: article writing. Beisha Tang: article writing. Jifeng Guo: article writing. All authors contributed to the article and approved the submitted version. Data Availability Data are available on reasonable request. References Adam H, Gopinath SCB, Md Arshad MK, Adam T, Parmin NA, Husein I, Hashim U (2023) An update on pathogenesis and clinical scenario for Parkinson's disease: diagnosis and treatment. 3 Biotech 13 (5):142. doi:10.1007/s13205-023-03553-8 Kalia LV, Lang AE (2015) Parkinson's disease. Lancet 386 (9996):896-912. doi:10.1016/s0140-6736(14)61393-3 Schapira AHV, Chaudhuri KR, Jenner P (2017) Non-motor features of Parkinson disease. Nat Rev Neurosci 18 (7):435-450. doi:10.1038/nrn.2017.62 Jan A, Gonçalves NP, Vaegter CB, Jensen PH, Ferreira N (2021) The Prion-Like Spreading of Alpha-Synuclein in Parkinson's Disease: Update on Models and Hypotheses. Int J Mol Sci 22 (15). doi:10.3390/ijms22158338 Dickson DW (2018) Neuropathology of Parkinson disease. Parkinsonism Relat Disord 46 Suppl 1 (Suppl 1):S30-s33. doi:10.1016/j.parkreldis.2017.07.033 Fayyad M, Salim S, Majbour N, Erskine D, Stoops E, Mollenhauer B, El-Agnaf OMA (2019) Parkinson's disease biomarkers based on α-synuclein. J Neurochem 150 (5):626-636. doi:10.1111/jnc.14809 Parnetti L, Gaetani L, Eusebi P, Paciotti S, Hansson O, El-Agnaf O, Mollenhauer B, Blennow K, Calabresi P (2019) CSF and blood biomarkers for Parkinson's disease. Lancet Neurol 18 (6):573-586. doi:10.1016/s1474-4422(19)30024-9 Hansson O (2021) Biomarkers for neurodegenerative diseases. Nat Med 27 (6):954-963. doi:10.1038/s41591-021-01382-x Chen R, Gu X, Wang X (2022) α-Synuclein in Parkinson's disease and advances in detection. Clin Chim Acta 529:76-86. doi:10.1016/j.cca.2022.02.006 Uversky VN, Li J, Fink AL (2001) Metal-triggered structural transformations, aggregation, and fibrillation of human alpha-synuclein. A possible molecular NK between Parkinson's disease and heavy metal exposure. J Biol Chem 276 (47):44284-44296. doi:10.1074/jbc.M105343200 Diaz-Ortiz ME, Seo Y, Posavi M, Carceles Cordon M, Clark E, Jain N, Charan R, Gallagher MD, Unger TL, Amari N, Skrinak RT, Davila-Rivera R, Brody EM, Han N, Zack R, Van Deerlin VM, Tropea TF, Luk KC, Lee EB, Weintraub D, Chen-Plotkin AS (2022) GPNMB confers risk for Parkinson's disease through interaction with α-synuclein. Science 377 (6608):eabk0637. doi:10.1126/science.abk0637 Budge KM, Neal ML, Richardson JR, Safadi FF (2018) Glycoprotein NMB: an Emerging Role in Neurodegenerative Disease. Mol Neurobiol 55 (6):5167-5176. doi:10.1007/s12035-017-0707-z Pihlstrøm L, Axelsson G, Bjørnarå KA, Dizdar N, Fardell C, Forsgren L, Holmberg B, Larsen JP, Linder J, Nissbrandt H, Tysnes OB, Ohman E, Dietrichs E, Toft M (2013) Supportive evidence for 11 loci from genome-wide association studies in Parkinson's disease. Neurobiol Aging 34 (6):1708.e1707-1713. doi:10.1016/j.neurobiolaging.2012.10.019 Santiago JA, Quinn JP, Potashkin JA (2023) Co-Expression Network Analysis Identifies Molecular Determinants of Loneliness Associated with Neuropsychiatric and Neurodegenerative Diseases. Int J Mol Sci 24 (6). doi:10.3390/ijms24065909 Chang D, Nalls MA, Hallgrímsdóttir IB, Hunkapiller J, van der Brug M, Cai F, Kerchner GA, Ayalon G, Bingol B, Sheng M, Hinds D, Behrens TW, Singleton AB, Bhangale TR, Graham RR (2017) A meta-analysis of genome-wide association studies identifies 17 new Parkinson's disease risk loci. Nat Genet 49 (10):1511-1516. doi:10.1038/ng.3955 Neal ML, Boyle AM, Budge KM, Safadi FF, Richardson JR (2018) The glycoprotein GPNMB attenuates astrocyte inflammatory responses through the CD44 receptor. J Neuroinflammation 15 (1):73. doi:10.1186/s12974-018-1100-1 Moloney EB, Moskites A, Ferrari EJ, Isacson O, Hallett PJ (2018) The glycoprotein GPNMB is selectively elevated in the substantia nigra of Parkinson's disease patients and increases after lysosomal stress. Neurobiol Dis 120:1-11. doi:10.1016/j.nbd.2018.08.013 Png G, Barysenka A, Repetto L, Navarro P, Shen X, Pietzner M, Wheeler E, Wareham NJ, Langenberg C, Tsafantakis E, Karaleftheri M, Dedoussis G, Mälarstig A, Wilson JF, Gilly A, Zeggini E (2021) Mapping the serum proteome to neurological diseases using whole genome sequencing. Nat Commun 12 (1):7042. doi:10.1038/s41467-021-27387-1 Zhu S, Wuolikainen A, Wu J, Öhman A, Wingsle G, Moritz T, Andersen PM, Forsgren L, Trupp M (2019) Targeted Multiple Reaction Monitoring Analysis of CSF Identifies UCHL1 and GPNMB as Candidate Biomarkers for ALS. J Mol Neurosci 69 (4):643-657. doi:10.1007/s12031-019-01411-y Emdin CA, Khera AV, Kathiresan S (2017) Mendelian Randomization. Jama 318 (19):1925-1926. doi:10.1001/jama.2017.17219 Yang C, Farias FHG, Ibanez L, Suhy A, Sadler B, Fernandez MV, Wang F, Bradley JL, Eiffert B, Bahena JA, Budde JP, Li Z, Dube U, Sung YJ, Mihindukulasuriya KA, Morris JC, Fagan AM, Perrin RJ, Benitez BA, Rhinn H, Harari O, Cruchaga C (2021) Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nat Neurosci 24 (9):1302-1312. doi:10.1038/s41593-021-00886-6 Ferkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, Gunnarsdottir K, Helgason A, Oddsson A, Halldorsson BV, Jensson BO, Zink F, Halldorsson GH, Masson G, Arnadottir GA, Katrinardottir H, Juliusson K, Magnusson MK, Magnusson OT, Fridriksdottir R, Saevarsdottir S, Gudjonsson SA, Stacey SN, Rognvaldsson S, Eiriksdottir T, Olafsdottir TA, Steinthorsdottir V, Tragante V, Ulfarsson MO, Stefansson H, Jonsdottir I, Holm H, Rafnar T, Melsted P, Saemundsdottir J, Norddahl GL, Lund SH, Gudbjartsson DF, Thorsteinsdottir U, Stefansson K (2021) Large-scale integration of the plasma proteome with genetics and disease. Nat Genet 53 (12):1712-1721. doi:10.1038/s41588-021-00978-w Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, Burgess S, Jiang T, Paige E, Surendran P, Oliver-Williams C, Kamat MA, Prins BP, Wilcox SK, Zimmerman ES, Chi A, Bansal N, Spain SL, Wood AM, Morrell NW, Bradley JR, Janjic N, Roberts DJ, Ouwehand WH, Todd JA, Soranzo N, Suhre K, Paul DS, Fox CS, Plenge RM, Danesh J, Runz H, Butterworth AS (2018) Genomic atlas of the human plasma proteome. Nature 558 (7708):73-79. doi:10.1038/s41586-018-0175-2 Nalls MA, Blauwendraat C, Vallerga CL, Heilbron K, Bandres-Ciga S, Chang D, Tan M, Kia DA, Noyce AJ, Xue A, Bras J, Young E, von Coelln R, Simón-Sánchez J, Schulte C, Sharma M, Krohn L, Pihlstrøm L, Siitonen A, Iwaki H, Leonard H, Faghri F, Gibbs JR, Hernandez DG, Scholz SW, Botia JA, Martinez M, Corvol JC, Lesage S, Jankovic J, Shulman LM, Sutherland M, Tienari P, Majamaa K, Toft M, Andreassen OA, Bangale T, Brice A, Yang J, Gan-Or Z, Gasser T, Heutink P, Shulman JM, Wood NW, Hinds DA, Hardy JA, Morris HR, Gratten J, Visscher PM, Graham RR, Singleton AB (2019) Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies. Lancet Neurol 18 (12):1091-1102. doi:10.1016/s1474-4422(19)30320-5 Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, Reeve MP, Laivuori H, Aavikko M, Kaunisto MA, Loukola A, Lahtela E, Mattsson H, Laiho P, Della Briotta Parolo P, Lehisto AA, Kanai M, Mars N, Rämö J, Kiiskinen T, Heyne HO, Veerapen K, Rüeger S, Lemmelä S, Zhou W, Ruotsalainen S, Pärn K, Hiekkalinna T, Koskelainen S, Paajanen T, Llorens V, Gracia-Tabuenca J, Siirtola H, Reis K, Elnahas AG, Sun B, Foley CN, Aalto-Setälä K, Alasoo K, Arvas M, Auro K, Biswas S, Bizaki-Vallaskangas A, Carpen O, Chen C-Y, Dada OA, Ding Z, Ehm MG, Eklund K, Färkkilä M, Finucane H, Ganna A, Ghazal A, Graham RR, Green EM, Hakanen A, Hautalahti M, Hedman ÅK, Hiltunen M, Hinttala R, Hovatta I, Hu X, Huertas-Vazquez A, Huilaja L, Hunkapiller J, Jacob H, Jensen J-N, Joensuu H, John S, Julkunen V, Jung M, Junttila J, Kaarniranta K, Kähönen M, Kajanne R, Kallio L, Kälviäinen R, Kaprio J, Kerimov N, Kettunen J, Kilpeläinen E, Kilpi T, Klinger K, Kosma V-M, Kuopio T, Kurra V, Laisk T, Laukkanen J, Lawless N, Liu A, Longerich S, Mägi R, Mäkelä J, Mäkitie A, Malarstig A, Mannermaa A, Maranville J, Matakidou A, Meretoja T, Mozaffari SV, Niemi MEK, Niemi M, Niiranen T, O´Donnell CJ, Obeidat Me, Okafo G, Ollila HM, Palomäki A, Palotie T, Partanen J, Paul DS, Pelkonen M, Pendergrass RK, Petrovski S, Pitkäranta A, Platt A, Pulford D, Punkka E, Pussinen P, Raghavan N, Rahimov F, Rajpal D, Renaud NA, Riley-Gillis B, Rodosthenous R, Saarentaus E, Salminen A, Salminen E, Salomaa V, Schleutker J, Serpi R, Shen H-y, Siegel R, Silander K, Siltanen S, Soini S, Soininen H, Sul JH, Tachmazidou I, Tasanen K, Tienari P, Toppila-Salmi S, Tukiainen T, Tuomi T, Turunen JA, Ulirsch JC, Vaura F, Virolainen P, Waring J, Waterworth D, Yang R, Nelis M, Reigo A, Metspalu A, Milani L, Esko T, Fox C, Havulinna AS, Perola M, Ripatti S, Jalanko A, Laitinen T, Mäkelä TP, Plenge R, McCarthy M, Runz H, Daly MJ, Palotie A, FinnGen (2023) FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613 (7944):508-518. doi:10.1038/s41586-022-05473-8 Blauwendraat C, Heilbron K, Vallerga CL, Bandres-Ciga S, von Coelln R, Pihlstrøm L, Simón-Sánchez J, Schulte C, Sharma M, Krohn L, Siitonen A, Iwaki H, Leonard H, Noyce AJ, Tan M, Gibbs JR, Hernandez DG, Scholz SW, Jankovic J, Shulman LM, Lesage S, Corvol JC, Brice A, van Hilten JJ, Marinus J, Eerola-Rautio J, Tienari P, Majamaa K, Toft M, Grosset DG, Gasser T, Heutink P, Shulman JM, Wood N, Hardy J, Morris HR, Hinds DA, Gratten J, Visscher PM, Gan-Or Z, Nalls MA, Singleton AB (2019) Parkinson's disease age at onset genome-wide association study: Defining heritability, genetic loci, and α-synuclein mechanisms. Mov Disord 34 (6):866-875. doi:10.1002/mds.27659 Iwaki H, Blauwendraat C, Leonard HL, Kim JJ, Liu G, Maple-Grødem J, Corvol JC, Pihlstrøm L, van Nimwegen M, Hutten SJ, Nguyen KH, Rick J, Eberly S, Faghri F, Auinger P, Scott KM, Wijeyekoon R, Van Deerlin VM, Hernandez DG, Gibbs JR, Chitrala KN, Day-Williams AG, Brice A, Alves G, Noyce AJ, Tysnes OB, Evans JR, Breen DP, Estrada K, Wegel CE, Danjou F, Simon DK, Andreassen O, Ravina B, Toft M, Heutink P, Bloem BR, Weintraub D, Barker RA, Williams-Gray CH, van de Warrenburg BP, Van Hilten JJ, Scherzer CR, Singleton AB, Nalls MA (2019) Genomewide association study of Parkinson's disease clinical biomarkers in 12 longitudinal patients' cohorts. Mov Disord 34 (12):1839-1850. doi:10.1002/mds.27845 Calabresi P, Mechelli A, Natale G, Volpicelli-Daley L, Di Lazzaro G, Ghiglieri V (2023) Alpha-synuclein in Parkinson's disease and other synucleinopathies: from overt neurodegeneration back to early synaptic dysfunction. Cell Death Dis 14 (3):176. doi:10.1038/s41419-023-05672-9 Ye H, Robak LA, Yu M, Cykowski M, Shulman JM (2023) Genetics and Pathogenesis of Parkinson's Syndrome. Annu Rev Pathol 18:95-121. doi:10.1146/annurev-pathmechdis-031521-034145 Mestre TA, Fereshtehnejad SM, Berg D, Bohnen NI, Dujardin K, Erro R, Espay AJ, Halliday G, van Hilten JJ, Hu MT, Jeon B, Klein C, Leentjens AFG, Marinus J, Mollenhauer B, Postuma R, Rajalingam R, Rodríguez-Violante M, Simuni T, Surmeier DJ, Weintraub D, McDermott MP, Lawton M, Marras C (2021) Parkinson's Disease Subtypes: Critical Appraisal and Recommendations. J Parkinsons Dis 11 (2):395-404. doi:10.3233/jpd-202472 Khodadadian A, Hemmati-Dinarvand M, Kalantary-Charvadeh A, Ghobadi A, Mazaheri M (2018) Candidate biomarkers for Parkinson's disease. Biomed Pharmacother 104:699-704. doi:10.1016/j.biopha.2018.05.026 Mollenhauer B, Zimmermann J, Sixel-Doering F, Focke NK, Wicke T, Ebentheuer J, Schaumburg M, Lang E, Friede T, Trenkwalder C, DeNoPa Study G (2019) Baseline predictors for progression 4 years after Parkinson's disease diagnosis in the De Novo Parkinson Cohort (DeNoPa). MOVEMENT DISORDERS 34 (1):67-77. doi:10.1002/mds.27492 Koeglsperger T, Rumpf SL, Schließer P, Struebing FL, Brendel M, Levin J, Trenkwalder C, Höglinger GU, Herms J (2023) Neuropathology of incidental Lewy body & prodromal Parkinson's disease. Mol Neurodegener 18 (1):32. doi:10.1186/s13024-023-00622-7 van de Berg WD, Hepp DH, Dijkstra AA, Rozemuller JA, Berendse HW, Foncke E (2012) Patterns of α-synuclein pathology in incidental cases and clinical subtypes of Parkinson's disease. Parkinsonism Relat Disord 18 Suppl 1:S28-30. doi:10.1016/s1353-8020(11)70011-6 Chang CW, Yang SY, Yang CC, Chang CW, Wu YR (2019) Plasma and Serum Alpha-Synuclein as a Biomarker of Diagnosis in Patients With Parkinson's Disease. Front Neurol 10:1388. doi:10.3389/fneur.2019.01388 Fan Z, Pan YT, Zhang ZY, Yang H, Yu SY, Zheng Y, Ma JH, Wang XM (2020) Systemic activation of NLRP3 inflammasome and plasma α-synuclein levels are correlated with motor severity and progression in Parkinson's disease. J Neuroinflammation 17 (1):11. doi:10.1186/s12974-019-1670-6 Chahine LM, Beach TG, Brumm MC, Adler CH, Coffey CS, Mosovsky S, Caspell-Garcia C, Serrano GE, Munoz DG, White CL, 3rd, Crary JF, Jennings D, Taylor P, Foroud T, Arnedo V, Kopil CM, Riley L, Dave KD, Mollenhauer B (2020) In vivo distribution of α-synuclein in multiple tissues and biofluids in Parkinson disease. Neurology 95 (9):e1267-e1284. doi:10.1212/wnl.0000000000010404 Schulz I, Kruse N, Gera RG, Kremer T, Cedarbaum J, Barbour R, Zago W, Schade S, Otte B, Bartl M, Hutten SJ, Trenkwalder C, Mollenhauer B (2021) Systematic Assessment of 10 Biomarker Candidates Focusing on alpha-Synuclein-Related Disorders. MOVEMENT DISORDERS 36 (12):2874-2887. doi:10.1002/mds.28738 Zubelzu M, Morera-Herreras T, Irastorza G, Gómez-Esteban JC, Murueta-Goyena A (2022) Plasma and serum alpha-synuclein as a biomarker in Parkinson's disease: A meta-analysis. Parkinsonism Relat Disord 99:107-115. doi:10.1016/j.parkreldis.2022.06.001 Zhang Q, Lin Z, He Y, Jiang J, Hu D (2023) Mendelian Randomization Analysis Reveals No Causal Relationship Between Plasma α-Synuclein and Parkinson's Disease. Mol Neurobiol 60 (4):2268-2276. doi:10.1007/s12035-023-03206-0 Wennström M, Surova Y, Hall S, Nilsson C, Minthon L, Boström F, Hansson O, Nielsen HM (2013) Low CSF levels of both α-synuclein and the α-synuclein cleaving enzyme neurosin in patients with synucleinopathy. PLoS One 8 (1):e53250. doi:10.1371/journal.pone.0053250 van Dijk KD, Bidinosti M, Weiss A, Raijmakers P, Berendse HW, van de Berg WD (2014) Reduced α-synuclein levels in cerebrospinal fluid in Parkinson's disease are unrelated to clinical and imaging measures of disease severity. Eur J Neurol 21 (3):388-394. doi:10.1111/ene.12176 Tokuda T, Salem SA, Allsop D, Mizuno T, Nakagawa M, Qureshi MM, Locascio JJ, Schlossmacher MG, El-Agnaf OM (2006) Decreased alpha-synuclein in cerebrospinal fluid of aged individuals and subjects with Parkinson's disease. Biochem Biophys Res Commun 349 (1):162-166. doi:10.1016/j.bbrc.2006.08.024 Kang JH, Irwin DJ, Chen-Plotkin AS, Siderowf A, Caspell C, Coffey CS, Waligórska T, Taylor P, Pan S, Frasier M, Marek K, Kieburtz K, Jennings D, Simuni T, Tanner CM, Singleton A, Toga AW, Chowdhury S, Mollenhauer B, Trojanowski JQ, Shaw LM (2013) Association of cerebrospinal fluid β-amyloid 1-42, T-tau, P-tau181, and α-synuclein levels with clinical features of drug-naive patients with early Parkinson disease. JAMA Neurol 70 (10):1277-1287. doi:10.1001/jamaneurol.2013.3861 Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigure1.tiff Supplementary Fig 1 The scatter plots and the leave-one-out plots of MR analyses (a) The scatter plot of MR analysis in PD and brain α-syn. (b) The leave-one-out plot of MR analysis in PD and brain α-syn. (c) The scatter plot of MR analysis in csf α-syn and H&Y stage at baseline. (d) The leave-one-out plot of MR analysis in csf α-syn and H&Y stage at baseline. (e) The scatter plot of MR analysis in csf α-syn and UPDRS Ⅲ at baseline. (f) The leave-one-out plot of MR analysis in csf α-syn and UPDRS Ⅲ at baseline. (g) The scatter plot of MR analysis in plasma α-syn and H&Y stage at baseline. (h) The leave-one-out plot of MR analysis in plasma α-syn and H&Y stage at baseline. supplementary.xlsx Cite Share Download PDF Status: Published Journal Publication published 11 Apr, 2025 Read the published version in Molecular Neurobiology → Version 1 posted Editorial decision: Revision requested 28 Nov, 2024 Reviews received at journal 27 Nov, 2024 Reviewers agreed at journal 18 Nov, 2024 Reviewers agreed at journal 07 Nov, 2024 Reviews received at journal 28 Oct, 2024 Reviews received at journal 27 Oct, 2024 Reviewers agreed at journal 27 Oct, 2024 Reviewers agreed at journal 24 Aug, 2024 Reviewers agreed at journal 13 Aug, 2024 Reviewers invited by journal 12 Jun, 2024 Editor assigned by journal 11 Jun, 2024 Submission checks completed at journal 09 Jun, 2024 First submitted to journal 04 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4525984","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":315246595,"identity":"d4a4ccf7-c38c-4604-b5fc-da58b792c1d2","order_by":0,"name":"Jifeng Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYJACgwQbIMneAOYwNhCnJQ1I8hwgQQsDA0iLRAKRWuQjkg8UPEiwyZOPfPz4Mw+DjeyGA8zPHuDTYngjLcEgISGt2PB2mpk0D0Oa8YYDbOYGeLXMyDEwSPxxOHHj7Bw2Zh6Gw4kbDvCwSRDUkpDwP3HjzDPMQIf9J6xFXgKs5UDifAkeBqDDDhDWYsDzDOSX5MQNPGlmknMMko1nHmYzw29Le/Ixwx8Jdonz2w8//vCmwk6273jzM/y2HGBgM4AyQCQQM+NTD7KlgYH5AZQxCkbBKBgFowA7AAAzb0lvs9JndgAAAABJRU5ErkJggg==","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":true,"prefix":"","firstName":"Jifeng","middleName":"","lastName":"Guo","suffix":""},{"id":315246596,"identity":"228eb0f0-7a63-4f54-9187-b9b7ad1a26bd","order_by":1,"name":"Lizhi Li","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Lizhi","middleName":"","lastName":"Li","suffix":""},{"id":315246597,"identity":"4088aa56-9f53-4478-8adc-78b9c50da56b","order_by":2,"name":"Zhenhua Liu","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Zhenhua","middleName":"","lastName":"Liu","suffix":""},{"id":315246598,"identity":"9aa120a0-fb5e-4671-a8f8-7931be7a4e89","order_by":3,"name":"Qian Xu","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Xu","suffix":""},{"id":315246599,"identity":"d25bc5c6-02e8-4e60-8087-1e99cab5e3e4","order_by":4,"name":"Xinxiang Yan","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xinxiang","middleName":"","lastName":"Yan","suffix":""},{"id":315246606,"identity":"72ca7d02-1390-4ba0-960a-08010f957b37","order_by":5,"name":"Beisha Tang","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Beisha","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2024-06-04 07:26:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4525984/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4525984/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12035-025-04928-z","type":"published","date":"2025-04-11T16:05:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58945035,"identity":"7ac9daed-5e10-49b6-b0bd-4c6f7f5e3c1f","added_by":"auto","created_at":"2024-06-24 12:23:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":123643,"visible":true,"origin":"","legend":"\u003cp\u003eThe framework of MR analysis\u003c/p\u003e\n\u003cp\u003eFirstly, MR analysis was performed to evaluate the causal relationship of α-syn and GPNMB with PD. Then, reverse MR analysis was utilized to detect the effect of PD on α-syn and GPNMB. All procedures must conform to 3 assumptions.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4525984/v1/6185a935edcf8583f661c160.jpg"},{"id":58945034,"identity":"b57b987d-abfb-45ee-b6d6-cad5f5e95492","added_by":"auto","created_at":"2024-06-24 12:23:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":307877,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of results from MR analysis to assess the causal relationship of α-syn and GPNMB with PD\u003c/p\u003e\n\u003cp\u003e* denotes suggestive significance.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4525984/v1/0431018c28c9ab8f751a3f57.jpg"},{"id":58944664,"identity":"7cb09b5a-331b-40e7-9085-9c3e0d4612c3","added_by":"auto","created_at":"2024-06-24 12:15:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":343241,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of results from reverse MR analysis to evaluate the causal relationship of PD with α-syn and GPNMB\u003c/p\u003e\n\u003cp\u003e* denotes suggestive significance.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4525984/v1/e0f540490fe807a4d126dbd7.jpg"},{"id":80558880,"identity":"689509d4-bb2a-48e0-b91c-97647a44a027","added_by":"auto","created_at":"2025-04-14 16:16:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1415730,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4525984/v1/733ffa8b-e972-465c-9102-692915b86402.pdf"},{"id":58944667,"identity":"06ad6ecc-60c2-41f2-bb18-63c7f4a33bef","added_by":"auto","created_at":"2024-06-24 12:15:00","extension":"tiff","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":873682,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Fig 1 The scatter plots and the leave-one-out plots of MR analyses\u003c/p\u003e\n\u003cp\u003e(a) The scatter plot of MR analysis in PD and brain α-syn. (b) The leave-one-out plot of MR analysis in PD and brain α-syn. (c) The scatter plot of MR analysis in csf α-syn and H\u0026amp;Y stage at baseline. (d) The leave-one-out plot of MR analysis in csf α-syn and H\u0026amp;Y stage at baseline. (e) The scatter plot of MR analysis in csf α-syn and UPDRS Ⅲ at baseline. (f) The leave-one-out plot of MR analysis in csf α-syn and UPDRS Ⅲ at baseline. (g) The scatter plot of MR analysis in plasma α-syn and H\u0026amp;Y stage at baseline. (h) The leave-one-out plot of MR analysis in plasma α-syn and H\u0026amp;Y stage at baseline.\u003c/p\u003e","description":"","filename":"Supplementaryfigure1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-4525984/v1/5cdb518c6f33dc1240080c7d.tiff"},{"id":58944663,"identity":"dca5d9d5-f581-417d-8b0e-cd38f41fd191","added_by":"auto","created_at":"2024-06-24 12:14:59","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":116608,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4525984/v1/ae4061b02f1d28191ebed77f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Mendelian Randomization Study on Association of Alpha-synuclein and GPNMB with Parkinson’s Disease Risk and Progression","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is one of the most common neurodegenerative diseases, with increasing incidence in the elderly[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A growing prevalence of PD is expected owing to aging populations worldwide, bringing severe societal, public, and personal burdens[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. PD manifests four cardinal motor symptoms (rest tremor, rigidity, bradykinesia and postural instability) and other non-motor symptoms such as cognitive impairment, depression, hyposmia, and sleep disturbance[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Various standard rating scales can assess motor and non-motor symptoms to understand PD patients\u0026rsquo; overall status better. Although the etiology of PD remains incompletely understood, the death of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the presence of Lewy body (LB) are primary pathological markers of PD, and LB is mainly composed of abnormally aggregated alpha-synuclein (α-syn)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Given the core role of α-syn in PD, cerebrospinal fluid (CSF) and plasma α-syn of PD patients have been measured over the past decades, providing a window for early diagnosis and monitoring disease progression of PD[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nevertheless, controversial results were observed due to various sample sizes, clinical heterogeneity and different measurement methods[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe triggering and driving event of α-syn misfolding and aggregation are poorly understood, with potential contributors including pesticides, organic solvents and metal irons[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Glycoprotein Nonmetastatic Melanoma Protein B protein (GPNMB), an endogenous glycoprotein involved in neuroinflammation, has been shown to interact with α-syn and facilitate its robust internalization and aggregation[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The \u003cem\u003eGPNMB\u003c/em\u003e gene was first reported to be associated with PD in a genome-wide association study (GWAS) in 2013[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], supported by the following research[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, GPNMB is capable of attenuating astrocyte inflammation selectively and is highly expressed in SN of individuals with PD[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and GPNMB levels in CSF and plasma correlate with PD and disease severity[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Though the above studies back GNPMB contributing to PD, contrary research results demonstrated no significant differences in CSF GPNMB between PD and healthy individuals[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Overall, observational studies of the level of α-syn and GPNMB on PD are inconsistent. Therefore, the study aims to elucidate the relationship of α-syn and GPNMB to PD and disease progression.\u003c/p\u003e \u003cp\u003eMendelian Randomization (MR) analysis employs the association of single nucleotide polymorphisms (SNP) and phenotypes to replace directly using data of exposure and outcome in randomized controlled trials. Based on the random allocation principle during allele allocation, MR effectively eliminates the bias of confounding factors, which is easier to carry out than RCTs. So, MR is an appropriate method to clarify the causal relationship between exposure and outcome. Given the convenience and effectiveness of MR, we performed MR analysis to determine the effects of GPNMB and α-syn on PD and its disease progression.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Data Sources\u003c/h2\u003e \u003cp\u003eInstrumental variables (IV) for MR analysis must conform to the following three core assumptions: (1) IVs must be strongly correlated with exposure factors; (2) IVs should not be associated with confounding factors; (3) IVs are associated with the outcome only through exposure[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the first stage of the study, we applied two-sample MR analysis to evaluate the causal association of α-syn and GPNMB with PD, and we then performed reverse MR analyses to verify the effect of PD on α-syn and GPNMB. The specific framework for the stage is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Next, two-sample MR analysis was employed to elucidate the influence of α-syn and GPNMB on PD age at onset (AAO) and disease progression.\u003c/p\u003e\u003cp\u003eAll summary statistics were obtained from previously published genome-wide association studies (GWAS), detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. GWAS data for α-syn and GPNMB were derived from three GWAS, named cohort 1, cohort 2, and cohort 3, respectively in the following content. Cohort1 used an aptamer-based platform to measure the levels of α-syn, GPNMB in CSF (n\u0026thinsp;=\u0026thinsp;835), plasma (n\u0026thinsp;=\u0026thinsp;529), and brain (n\u0026thinsp;=\u0026thinsp;380) samples from individuals with Alzheimer's disease (AD) and cognitively normal controls, and all participants in the GWAS were of European ancestry[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Cohort 2 measured plasma α-syn and GPNMB levels in 35287 Icelanders[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and cohort 3 measured plasma levels of both proteins in 3301 healthy participants of European ancestry[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. GWAS summary statistics for PD were sourced from a meta-analysis of 17 datasets from European ancestry samples conducted by Nalls et al., and the statistics applied in the study excluded data from Nalls et al. 2014, 23andMe post-Chang et al. 2017 and Web-Based Study of Parkinson\u0026rsquo;s Disease (PDWBS)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, we incorporated PD GWAS data from the FinnGen Consortium as another PD outcome for MR analysis[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The GWAS of PD AAO was obtained from International Parkinson\u0026rsquo;s Disease Genomics Consortium (iPDGC). The GWAS of PD disease progression included 12 longitudinal cohorts. All participants in these two GWAS were also of European ancestry[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\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\u003eThe specific description of GWAS\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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsortium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size (Cases/controls/others)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJournal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain GPNMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e380(248/24/108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNature neuroscience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF GPNMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e835(217/614/4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNature neuroscience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePlasma GPNMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e529(201/612/4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNature neuroscience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe INTERVAL study,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3301 healthy participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Icelandic Cancer Project (52%);\u003c/p\u003e \u003cp\u003edeCODE genetics, Reykjav\u0026iacute;k, Iceland (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNature genetics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain α-syn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e380(248/24/108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNature neuroscience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSF α-syn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e835(217/614/4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNature neuroscience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePlasma α-syn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e529(168/357/4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNature neuroscience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe INTERVAL study,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3301 healthy participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Icelandic Cancer Project (52%);\u003c/p\u003e \u003cp\u003edeCODE genetics, Reykjav\u0026iacute;k, Iceland (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNature genetics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParkinson\u0026rsquo;s disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternational Parkinson\u0026rsquo;s Disease Genomics Consortium (IPDGC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e482,730 (33,674/449,056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe Lancet. Neurology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinnish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e342499(3767/338732)\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\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAAO of PD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPDGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMovement disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD clinical features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDATATOP; DIGPD_chip; DIGPD_chip; HBS; NET-PD_LS1; OSLO; PARKFIT; PARKWEST; PDBP; PICNICS; PPMI; PRECEPT/POSTCEPT; PROPARK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMovement disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: CSF: Cerebrospinal fluid; AAO: Age at onset; PD: Parkinson\u0026rsquo;s disease.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSelection of Genetic Instrumental Variables\u003c/h2\u003e \u003cp\u003eGenetic Instrumental Variables were extracted from selected GWAS data. Initially, we set the significance threshold at 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, but this yielded few or no satisfactory SNPs. Therefore, we relaxed the threshold to 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e to get enough SNPs for MR analysis. Eligible SNPs were then clumped under the condition of r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 within 10Mb, according to the 1000 Genomes Project linkage disequilibrium (LD) structure. Each remaining SNP was checked using PhenoScanner v2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.cam.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.phenoscanner.medschl.cam.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to exclude SNPs related to other phenotypes that could contribute to outcomes of the study (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). The \u003cem\u003eF\u003c/em\u003e statistic of each selected SNP was calculated, and only SNPs with \u003cem\u003eF\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;10 were regarded as sufficient statistical strength for subsequent MR analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMendelian Randomization Analysis\u003c/h2\u003e \u003cp\u003eFive methods of MR analysis were performed to evaluate the causative association between exposures and outcomes, including the random effects inverse variance weighted (IVW), MR-Egger, weighted median, weighted mode and simple mode. However, IVW was considered the primary method based on the assumption that all IVs used for analysis were valid, and the other four methods were adopted to validate the results of IVW. After the Bonferroni correction, we set a statistically significant \u003cem\u003eP\u003c/em\u003e-value threshold at less than 4.630 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;4 (0.05/108), and a \u003cem\u003eP\u003c/em\u003e-value below 0.05 was defined as suggestively significant. Furthermore, we performed MR-PRESSO to find out outliers among the IVs. Once detecting outliers, we ruled out the outliers and re-performed MR analysis to reduce the effects of horizontal pleiotropy. Besides, Cochran\u0026rsquo;s Q test, leave-one-out methods and MR Egger regression were recommended to assess heterogeneity and pleiotropy during MR analysis. Statistical analysis was achieved using R (TwoSampleMR version 0.5.6).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSNPs and the results of MR analysis are detailed in Supplementary Tables, with \u003cem\u003eF\u003c/em\u003e statistics of IVs greater than the threshold of 10.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of α-syn and GPNMB with PD\u003c/h2\u003e \u003cp\u003eAs shown in Supplementary Tables, no significant association were detected between the levels of α-syn and GPNMB in brain, CSF and plasma with PD under the model of IVW method (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), nor under Weighted median or other MR analyses. It is well known that α-syn aggregation is a remarkable pathologic change in PD ahead of clinical characteristics[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our results are controversial with previous studies, likely because the brain samples in cohort 1 were taken from the parietal lobe cortex rather than SNpc.\u003c/p\u003e \u003cp\u003eRegarding the results of the reverse MR analysis, a suggestive significance between PD and brain α-syn was observed under IVW method (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022, OR\u0026thinsp;=\u0026thinsp;0.95, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and Weighted median method (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043, OR\u0026thinsp;=\u0026thinsp;0.94, Supplementary Tables). In addition, MR-PRESSO identified no outliers among the SNPs, with a \u003cem\u003eP\u003c/em\u003e-value of 0.847, indicating no horizontal pleiotropy in the results. Furthermore, no heterogeneity and horizontal pleiotropy were detected by Cochran\u0026rsquo;s Q test (Q\u0026thinsp;=\u0026thinsp;15.84, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eQ\u003c/em\u003e\u003c/sub\u003e = 0.824) and MR-Egger_intercept test (intercept = -0.009, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eintercept\u003c/sub\u003e = 0.183, Supplementary Tables). The scatter plot and the leave-one-out plot for PD and brain α-syn are displayed in Supplementary Fig.\u0026nbsp;1A and Supplementary Fig.\u0026nbsp;1B.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of α-syn and GPNMB with PD disease progression\u003c/h2\u003e \u003cp\u003eThe IVW method indicated that levels of α-syn and GPNMB in brain, CSF and plasma did not affect AAO of PD, in line with the results of MR-Egger method and Weighted median method. In terms of their effects on baseline clinical features of PD, IVW model identified a suggestive causal association of CSF α-syn with the Unified Parkinson's Disease Rating Scale Ⅲ (UPDRS Ⅲ) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049, OR\u0026thinsp;=\u0026thinsp;0.33), Hoehn and Yahr (H\u0026amp;Y) stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032, OR\u0026thinsp;=\u0026thinsp;0.62) and daytime sleepiness (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012, OR\u0026thinsp;=\u0026thinsp;56.62). Moreover, plasma α-syn showed a suggestive association with H\u0026amp;Y stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, OR\u0026thinsp;=\u0026thinsp;0.93). However, none of these associations remained significant after the Bonferroni correction. The results of IVW, MR-Egger and Weighted median methods indicated that higher levels of CSF α-syn were related to lower UPDRS Ⅲ scores, and no heterogeneity and horizontal pleiotropy was inferred by Cochran\u0026rsquo;s Q test (Q\u0026thinsp;=\u0026thinsp;1.62, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eQ\u003c/em\u003e\u003c/sub\u003e = 0.805) and MR-Egger_intercept test (intercept = -0.049, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eintercept\u003c/em\u003e\u003c/sub\u003e = 0.460, Supplementary Tables). Additionally, higher levels of CSF α-syn were suggestively associated with lower H\u0026amp;Y stages under IVW model, without heterogeneity and horizontal pleiotropy. Regarding the association between CSF α-syn and daytime sleepiness, no causative relationship was inferred, as indicated by intercept = -0.132 in MR Egger analysis. The role of plasma α-syn in H\u0026amp;Y stage is doubted due to inconsistent results across the three cohorts, with only a suggestive association observed in cohort 3. Despite this, both IVW and MR Egger methods provided evidence for the association, and MR-PRESSO (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.350) and Cochran\u0026rsquo;s Q test (Q\u0026thinsp;=\u0026thinsp;15.11, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eQ\u003c/sub\u003e = 0.235) of cohort 3 revealed no heterogeneity and horizontal pleiotropy of results. Scatter plots and leave-one-out plots of the above results were displayed in Supplementary Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eResults of IVW model for disease progression during follow-up implied that brain α-syn and plasma GPNMB of cohort 1 were associated with REM sleep behavior disorder (RBD) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, OR\u0026thinsp;=\u0026thinsp;0.02) and the Schwab and England Activities of Daily Living scale 70 (SEADL70) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044, OR\u0026thinsp;=\u0026thinsp;0.01), respectively. Nevertheless, MR Egger_intercept of these associations was greater than 0.05, meaning that the causative association is untrustworthy.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study explored the causal relationship of GPNMB and α-syn with PD and its disease progression using two-sample MR to seek promising biomarkers for early diagnosis and monitoring the disease progression of PD. Above all, we found that CSF α-syn correlated to UPDRS Ⅲ and H\u0026amp;Y stage, and plasma α-syn was associated with H\u0026amp;Y stage suggestively on baseline clinical features. Furthermore, reverse MR analysis indicated that PD mediates the formation of α-syn in the parietal lobe cortex. However, no causal association was detected between GPNMB in brain parietal lobe cortex, CSF and plasma with PD risk and disease progression, suggesting that GPNMB is not an appropriate biomarker for PD even though it drives aggregation of α-syn and PD pathology. In harmony with previous studies, our findings underscore α-syn, particularly CSF α-syn, as a considerable potential biomarker that correlates with motor symptoms of PD. Unfortunately, we failed to discover any association of GPNMB and α-syn with disease progression during follow-up.\u003c/p\u003e \u003cp\u003eAt present, the pathogenic mechanism of PD is not thoroughly investigated due to its complexity. Neurologists make a diagnosis of PD primarily based on clinical motor symptoms and physical examination. Relying on motor features for diagnosis poses a challenge for early detection of PD, as motor symptoms typically manifest only after more than half of the dopamine neurons are lost[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In addition, the clinical presentations of PD are tremendously heterogeneous, accompanied by significant variability in disease progression and treatment responses[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, identifying biomarkers capable of diagnosing PD before the onset of motor symptoms and correlating with PD subtypes is essential. Currently, observational studies focus on biomarkers in CSF and plasma for PD diagnosis and prognosis, including α-syn, neurofilament light chain (NfL), amyloid-beta 42 (Aβ42), tau, lysosomal enzymes, and so on[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Among them, α-syn is the most potential marker due to its significant role in PD pathogenesis. Recently, GPNMB has also been shown to be elevated in PD and reflects the clinical severity of PD[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eα-syn is encoded by the \u003cem\u003eSNCA\u003c/em\u003e gene, a disease-causing gene of PD, which misfolds and aggregates subsequently into toxic forms, resulting in dopaminergic neuron degradation[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The formation and gradual development of aggregated α-syn are involved in the onset and disease progression of PD. Therefore, α-syn levels in various biofluids have been investigated to distinguish PD from healthy controls and other neurodegenerative diseases, as well as to predict disease progression[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Contradictory results of α-syn in CSF and plasma have been reported. As far as plasma α-syn is concerned, most studies have illustrated elevated levels in PD patients compared to healthy controls[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], whereas decreased and similar levels of α-syn in PD were also observed[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Zubelzu et al. conducted a meta-analysis on plasma α-syn levels in individuals with PD, supporting the idea that plasma α-syn levels increase in the early stages of PD[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Conversely, a Mendelian Randomization analysis exploring the relationship between plasma α-syn and PD revealed no causative relationship, coinciding with our results[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, the levels of plasma α-syn are also significantly related to clinical features of PD, including lower age, shorter disease duration and milder motor impairment[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and the results of the study also showed that higher levels of plasma α-syn were associated with a lower H\u0026amp;Y stage.\u003c/p\u003e \u003cp\u003eConcerning the effect of CSF α-syn on PD, the majority of studies endorse that CSF α-syn concentrations are lower in subjects with PD compared to normal controls[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The relationship between CSF α-syn levels and disease severity remains elusive, but CSF α-syn levels are likely to correlate with H\u0026amp;Y stage and postural instability[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Our research suggests that increased CSF α-syn is linked with milder motor disorders and lower H\u0026amp;Y stages in PD patients. Still, no association between α-syn in CSF and PD was detected. In general, the correlation between α-syn in CSF and plasma and the degree of motor impairment is consistent with previous research, but regarding the association between α-syn with PD, the results demonstrated no correlation. We speculate that the inverse correlation between motor impairment in PD and α-syn in CSF and plasma is because plenty of α-syn form LBs in neurons of patients with PD, which leads to a lower level of α-syn in CSF and plasma and severe motor disorder.\u003c/p\u003e \u003cp\u003eWe presented the results that GPNMB in brain, CSF and plasma did not correlate with PD or its disease progression, incompatible with the observational study, which illustrated elevated plasma GPNMB is associated with PD and more severe motor impairment[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The reverse MR analysis also revealed no causal relationship between PD and GPNMB in the parietal lobe cortex, CSF and plasma. The inconsistent findings may be attributed to the following reasons. Firstly, the observational studies recruited only 731 subjects diagnosed as PD and 59 controls, whose inadequate sample size would bring inaccurate conclusions. Secondly, the detection level of GPNMB is influenced by examination methods. The observational study used Enzyme-Linked Immunosorbent Assay (ELISA) to measure the level of GPNMB, which differed from the methods of GWAS. Overall, it is noted that GPNMB interacting with α-syn plays a crucial role in the onset of PD and elevated expression of GPNMB in SN of PD patients[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, using MR analysis, we found that GPNMB is unsuitable for diagnosing PD and monitoring its disease progression.\u003c/p\u003e \u003cp\u003eOur research is an MR study to address the relationship of α-syn and GPNMB with PD, and we employed several GWAS and only reserved SNP with \u003cem\u003eF\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;10 to ensure that the results were robust. However, several limitations must be mentioned. First of all, the population of GWAS adopted in the study are primarily of European ancestry, meaning that findings for the study cannot be extended to other populations directly. Next, the sample size of GWAS for α-syn and GPNMB is relatively small and available IVs for several MR analyses is fewer, affecting the credibility of MR results. Lastly, our research only detects a suggestive association of α-syn with PD phenotypes, and the association did not exist after the Bonferroni correction. Therefore, more cohort and multicenter research are warranted to validate our findings.\u003c/p\u003e \u003cp\u003eIn short, the study found that CSF and plasma α-syn were related to the severity of motor impairment in PD patients suggestively, whereas there was no correlation between GPNMB and PD based on MR analysis. These results provide an insight into profoundly understanding the role of α-syn and GPNMB in the pathogenesis of PD and give evidence for the selection of PD biomarkers. However, there are some limitations in our study, and the results need more research to warrant.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatements \u0026amp; Declarations\u003c/h2\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSummary statistics of PD (iPDGC) and AAO and disease progression of PD were downloaded from iPDGC (https://pdgenetics.org/). Summary statistics of PD (FinnGen) were downloaded from FinnGen (https://www.finngen.fi/fi) and We want to acknowledge the participants and investigators of the FinnGen study. The summary GWAS data of cohort 1 could be acquired from the corresponding author. The GWAS summary statistics data of \u0026alpha;-syn and GPNMB (cohort 2 and cohort 3)was obtained from websites (https://www.decode.com/summarydata/and http://www.phpc.cam.ac.uk/ceu/proteins/, respectively).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is a Mendelian Randomization Study, whose data were from public GWAS. Therefore, all relevant ethics statements are shown in the original GWAS, and the study did not approve an ethnical statement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Key Research and Development Program of China (Grant No. 2021YFC2501204); Technology Major Project of Hunan Provincial Science and Technology Department (Grant No.2021SK1010); National Natural Science Foundation of China (Grant No.82071439, Grant No. 82271281, Grant No. 81974202,Grant No.U20A20355); Innovative team program from Department of Science \u0026amp; Technology of Hunan Province (Grant No.2019RS1010), and Innovation-driven Team Project from Central South University (Grant No.2020CX016).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLizhi Li: conception, statistical analysis and article writing. Xinxiang Yan: article writing. Qian Xu: article writing. Zhenhua Liu: article writing. Beisha Tang: article writing. Jifeng Guo: article writing.\u0026nbsp;All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available on reasonable request.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdam H, Gopinath SCB, Md Arshad MK, Adam T, Parmin NA, Husein I, Hashim U (2023) An update on pathogenesis and clinical scenario for Parkinson\u0026apos;s disease: diagnosis and treatment. 3 Biotech 13 (5):142. doi:10.1007/s13205-023-03553-8\u003c/li\u003e\n\u003cli\u003eKalia LV, Lang AE (2015) Parkinson\u0026apos;s disease. Lancet 386 (9996):896-912. doi:10.1016/s0140-6736(14)61393-3\u003c/li\u003e\n\u003cli\u003eSchapira AHV, Chaudhuri KR, Jenner P (2017) Non-motor features of Parkinson disease. Nat Rev Neurosci 18 (7):435-450. doi:10.1038/nrn.2017.62\u003c/li\u003e\n\u003cli\u003eJan A, Gon\u0026ccedil;alves NP, Vaegter CB, Jensen PH, Ferreira N (2021) The Prion-Like Spreading of Alpha-Synuclein in Parkinson\u0026apos;s Disease: Update on Models and Hypotheses. Int J Mol Sci 22 (15). doi:10.3390/ijms22158338\u003c/li\u003e\n\u003cli\u003eDickson DW (2018) Neuropathology of Parkinson disease. Parkinsonism Relat Disord 46 Suppl 1 (Suppl 1):S30-s33. doi:10.1016/j.parkreldis.2017.07.033\u003c/li\u003e\n\u003cli\u003eFayyad M, Salim S, Majbour N, Erskine D, Stoops E, Mollenhauer B, El-Agnaf OMA (2019) Parkinson\u0026apos;s disease biomarkers based on \u0026alpha;-synuclein. J Neurochem 150 (5):626-636. doi:10.1111/jnc.14809\u003c/li\u003e\n\u003cli\u003eParnetti L, Gaetani L, Eusebi P, Paciotti S, Hansson O, El-Agnaf O, Mollenhauer B, Blennow K, Calabresi P (2019) CSF and blood biomarkers for Parkinson\u0026apos;s disease. Lancet Neurol 18 (6):573-586. doi:10.1016/s1474-4422(19)30024-9\u003c/li\u003e\n\u003cli\u003eHansson O (2021) Biomarkers for neurodegenerative diseases. Nat Med 27 (6):954-963. doi:10.1038/s41591-021-01382-x\u003c/li\u003e\n\u003cli\u003eChen R, Gu X, Wang X (2022) \u0026alpha;-Synuclein in Parkinson\u0026apos;s disease and advances in detection. Clin Chim Acta 529:76-86. doi:10.1016/j.cca.2022.02.006\u003c/li\u003e\n\u003cli\u003eUversky VN, Li J, Fink AL (2001) Metal-triggered structural transformations, aggregation, and fibrillation of human alpha-synuclein. A possible molecular NK between Parkinson\u0026apos;s disease and heavy metal exposure. J Biol Chem 276 (47):44284-44296. doi:10.1074/jbc.M105343200\u003c/li\u003e\n\u003cli\u003eDiaz-Ortiz ME, Seo Y, Posavi M, Carceles Cordon M, Clark E, Jain N, Charan R, Gallagher MD, Unger TL, Amari N, Skrinak RT, Davila-Rivera R, Brody EM, Han N, Zack R, Van Deerlin VM, Tropea TF, Luk KC, Lee EB, Weintraub D, Chen-Plotkin AS (2022) GPNMB confers risk for Parkinson\u0026apos;s disease through interaction with \u0026alpha;-synuclein. Science 377 (6608):eabk0637. doi:10.1126/science.abk0637\u003c/li\u003e\n\u003cli\u003eBudge KM, Neal ML, Richardson JR, Safadi FF (2018) Glycoprotein NMB: an Emerging Role in Neurodegenerative Disease. Mol Neurobiol 55 (6):5167-5176. doi:10.1007/s12035-017-0707-z\u003c/li\u003e\n\u003cli\u003ePihlstr\u0026oslash;m L, Axelsson G, Bj\u0026oslash;rnar\u0026aring; KA, Dizdar N, Fardell C, Forsgren L, Holmberg B, Larsen JP, Linder J, Nissbrandt H, Tysnes OB, Ohman E, Dietrichs E, Toft M (2013) Supportive evidence for 11 loci from genome-wide association studies in Parkinson\u0026apos;s disease. Neurobiol Aging 34 (6):1708.e1707-1713. doi:10.1016/j.neurobiolaging.2012.10.019\u003c/li\u003e\n\u003cli\u003eSantiago JA, Quinn JP, Potashkin JA (2023) Co-Expression Network Analysis Identifies Molecular Determinants of Loneliness Associated with Neuropsychiatric and Neurodegenerative Diseases. Int J Mol Sci 24 (6). doi:10.3390/ijms24065909\u003c/li\u003e\n\u003cli\u003eChang D, Nalls MA, Hallgr\u0026iacute;msd\u0026oacute;ttir IB, Hunkapiller J, van der Brug M, Cai F, Kerchner GA, Ayalon G, Bingol B, Sheng M, Hinds D, Behrens TW, Singleton AB, Bhangale TR, Graham RR (2017) A meta-analysis of genome-wide association studies identifies 17 new Parkinson\u0026apos;s disease risk loci. Nat Genet 49 (10):1511-1516. doi:10.1038/ng.3955\u003c/li\u003e\n\u003cli\u003eNeal ML, Boyle AM, Budge KM, Safadi FF, Richardson JR (2018) The glycoprotein GPNMB attenuates astrocyte inflammatory responses through the CD44 receptor. J Neuroinflammation 15 (1):73. doi:10.1186/s12974-018-1100-1\u003c/li\u003e\n\u003cli\u003eMoloney EB, Moskites A, Ferrari EJ, Isacson O, Hallett PJ (2018) The glycoprotein GPNMB is selectively elevated in the substantia nigra of Parkinson\u0026apos;s disease patients and increases after lysosomal stress. Neurobiol Dis 120:1-11. doi:10.1016/j.nbd.2018.08.013\u003c/li\u003e\n\u003cli\u003ePng G, Barysenka A, Repetto L, Navarro P, Shen X, Pietzner M, Wheeler E, Wareham NJ, Langenberg C, Tsafantakis E, Karaleftheri M, Dedoussis G, M\u0026auml;larstig A, Wilson JF, Gilly A, Zeggini E (2021) Mapping the serum proteome to neurological diseases using whole genome sequencing. Nat Commun 12 (1):7042. doi:10.1038/s41467-021-27387-1\u003c/li\u003e\n\u003cli\u003eZhu S, Wuolikainen A, Wu J, \u0026Ouml;hman A, Wingsle G, Moritz T, Andersen PM, Forsgren L, Trupp M (2019) Targeted Multiple Reaction Monitoring Analysis of CSF Identifies UCHL1 and GPNMB as Candidate Biomarkers for ALS. J Mol Neurosci 69 (4):643-657. doi:10.1007/s12031-019-01411-y\u003c/li\u003e\n\u003cli\u003eEmdin CA, Khera AV, Kathiresan S (2017) Mendelian Randomization. Jama 318 (19):1925-1926. doi:10.1001/jama.2017.17219\u003c/li\u003e\n\u003cli\u003eYang C, Farias FHG, Ibanez L, Suhy A, Sadler B, Fernandez MV, Wang F, Bradley JL, Eiffert B, Bahena JA, Budde JP, Li Z, Dube U, Sung YJ, Mihindukulasuriya KA, Morris JC, Fagan AM, Perrin RJ, Benitez BA, Rhinn H, Harari O, Cruchaga C (2021) Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nat Neurosci 24 (9):1302-1312. doi:10.1038/s41593-021-00886-6\u003c/li\u003e\n\u003cli\u003eFerkingstad E, Sulem P, Atlason BA, Sveinbjornsson G, Magnusson MI, Styrmisdottir EL, Gunnarsdottir K, Helgason A, Oddsson A, Halldorsson BV, Jensson BO, Zink F, Halldorsson GH, Masson G, Arnadottir GA, Katrinardottir H, Juliusson K, Magnusson MK, Magnusson OT, Fridriksdottir R, Saevarsdottir S, Gudjonsson SA, Stacey SN, Rognvaldsson S, Eiriksdottir T, Olafsdottir TA, Steinthorsdottir V, Tragante V, Ulfarsson MO, Stefansson H, Jonsdottir I, Holm H, Rafnar T, Melsted P, Saemundsdottir J, Norddahl GL, Lund SH, Gudbjartsson DF, Thorsteinsdottir U, Stefansson K (2021) Large-scale integration of the plasma proteome with genetics and disease. Nat Genet 53 (12):1712-1721. doi:10.1038/s41588-021-00978-w\u003c/li\u003e\n\u003cli\u003eSun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, Burgess S, Jiang T, Paige E, Surendran P, Oliver-Williams C, Kamat MA, Prins BP, Wilcox SK, Zimmerman ES, Chi A, Bansal N, Spain SL, Wood AM, Morrell NW, Bradley JR, Janjic N, Roberts DJ, Ouwehand WH, Todd JA, Soranzo N, Suhre K, Paul DS, Fox CS, Plenge RM, Danesh J, Runz H, Butterworth AS (2018) Genomic atlas of the human plasma proteome. Nature 558 (7708):73-79. doi:10.1038/s41586-018-0175-2\u003c/li\u003e\n\u003cli\u003eNalls MA, Blauwendraat C, Vallerga CL, Heilbron K, Bandres-Ciga S, Chang D, Tan M, Kia DA, Noyce AJ, Xue A, Bras J, Young E, von Coelln R, Sim\u0026oacute;n-S\u0026aacute;nchez J, Schulte C, Sharma M, Krohn L, Pihlstr\u0026oslash;m L, Siitonen A, Iwaki H, Leonard H, Faghri F, Gibbs JR, Hernandez DG, Scholz SW, Botia JA, Martinez M, Corvol JC, Lesage S, Jankovic J, Shulman LM, Sutherland M, Tienari P, Majamaa K, Toft M, Andreassen OA, Bangale T, Brice A, Yang J, Gan-Or Z, Gasser T, Heutink P, Shulman JM, Wood NW, Hinds DA, Hardy JA, Morris HR, Gratten J, Visscher PM, Graham RR, Singleton AB (2019) Identification of novel risk loci, causal insights, and heritable risk for Parkinson\u0026apos;s disease: a meta-analysis of genome-wide association studies. Lancet Neurol 18 (12):1091-1102. doi:10.1016/s1474-4422(19)30320-5\u003c/li\u003e\n\u003cli\u003eKurki MI, Karjalainen J, Palta P, Sipil\u0026auml; TP, Kristiansson K, Donner KM, Reeve MP, Laivuori H, Aavikko M, Kaunisto MA, Loukola A, Lahtela E, Mattsson H, Laiho P, Della Briotta Parolo P, Lehisto AA, Kanai M, Mars N, R\u0026auml;m\u0026ouml; J, Kiiskinen T, Heyne HO, Veerapen K, R\u0026uuml;eger S, Lemmel\u0026auml; S, Zhou W, Ruotsalainen S, P\u0026auml;rn K, Hiekkalinna T, Koskelainen S, Paajanen T, Llorens V, Gracia-Tabuenca J, Siirtola H, Reis K, Elnahas AG, Sun B, Foley CN, Aalto-Set\u0026auml;l\u0026auml; K, Alasoo K, Arvas M, Auro K, Biswas S, Bizaki-Vallaskangas A, Carpen O, Chen C-Y, Dada OA, Ding Z, Ehm MG, Eklund K, F\u0026auml;rkkil\u0026auml; M, Finucane H, Ganna A, Ghazal A, Graham RR, Green EM, Hakanen A, Hautalahti M, Hedman \u0026Aring;K, Hiltunen M, Hinttala R, Hovatta I, Hu X, Huertas-Vazquez A, Huilaja L, Hunkapiller J, Jacob H, Jensen J-N, Joensuu H, John S, Julkunen V, Jung M, Junttila J, Kaarniranta K, K\u0026auml;h\u0026ouml;nen M, Kajanne R, Kallio L, K\u0026auml;lvi\u0026auml;inen R, Kaprio J, Kerimov N, Kettunen J, Kilpel\u0026auml;inen E, Kilpi T, Klinger K, Kosma V-M, Kuopio T, Kurra V, Laisk T, Laukkanen J, Lawless N, Liu A, Longerich S, M\u0026auml;gi R, M\u0026auml;kel\u0026auml; J, M\u0026auml;kitie A, Malarstig A, Mannermaa A, Maranville J, Matakidou A, Meretoja T, Mozaffari SV, Niemi MEK, Niemi M, Niiranen T, O\u0026acute;Donnell CJ, Obeidat Me, Okafo G, Ollila HM, Palom\u0026auml;ki A, Palotie T, Partanen J, Paul DS, Pelkonen M, Pendergrass RK, Petrovski S, Pitk\u0026auml;ranta A, Platt A, Pulford D, Punkka E, Pussinen P, Raghavan N, Rahimov F, Rajpal D, Renaud NA, Riley-Gillis B, Rodosthenous R, Saarentaus E, Salminen A, Salminen E, Salomaa V, Schleutker J, Serpi R, Shen H-y, Siegel R, Silander K, Siltanen S, Soini S, Soininen H, Sul JH, Tachmazidou I, Tasanen K, Tienari P, Toppila-Salmi S, Tukiainen T, Tuomi T, Turunen JA, Ulirsch JC, Vaura F, Virolainen P, Waring J, Waterworth D, Yang R, Nelis M, Reigo A, Metspalu A, Milani L, Esko T, Fox C, Havulinna AS, Perola M, Ripatti S, Jalanko A, Laitinen T, M\u0026auml;kel\u0026auml; TP, Plenge R, McCarthy M, Runz H, Daly MJ, Palotie A, FinnGen (2023) FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613 (7944):508-518. doi:10.1038/s41586-022-05473-8\u003c/li\u003e\n\u003cli\u003eBlauwendraat C, Heilbron K, Vallerga CL, Bandres-Ciga S, von Coelln R, Pihlstr\u0026oslash;m L, Sim\u0026oacute;n-S\u0026aacute;nchez J, Schulte C, Sharma M, Krohn L, Siitonen A, Iwaki H, Leonard H, Noyce AJ, Tan M, Gibbs JR, Hernandez DG, Scholz SW, Jankovic J, Shulman LM, Lesage S, Corvol JC, Brice A, van Hilten JJ, Marinus J, Eerola-Rautio J, Tienari P, Majamaa K, Toft M, Grosset DG, Gasser T, Heutink P, Shulman JM, Wood N, Hardy J, Morris HR, Hinds DA, Gratten J, Visscher PM, Gan-Or Z, Nalls MA, Singleton AB (2019) Parkinson\u0026apos;s disease age at onset genome-wide association study: Defining heritability, genetic loci, and \u0026alpha;-synuclein mechanisms. Mov Disord 34 (6):866-875. doi:10.1002/mds.27659\u003c/li\u003e\n\u003cli\u003eIwaki H, Blauwendraat C, Leonard HL, Kim JJ, Liu G, Maple-Gr\u0026oslash;dem J, Corvol JC, Pihlstr\u0026oslash;m L, van Nimwegen M, Hutten SJ, Nguyen KH, Rick J, Eberly S, Faghri F, Auinger P, Scott KM, Wijeyekoon R, Van Deerlin VM, Hernandez DG, Gibbs JR, Chitrala KN, Day-Williams AG, Brice A, Alves G, Noyce AJ, Tysnes OB, Evans JR, Breen DP, Estrada K, Wegel CE, Danjou F, Simon DK, Andreassen O, Ravina B, Toft M, Heutink P, Bloem BR, Weintraub D, Barker RA, Williams-Gray CH, van de Warrenburg BP, Van Hilten JJ, Scherzer CR, Singleton AB, Nalls MA (2019) Genomewide association study of Parkinson\u0026apos;s disease clinical biomarkers in 12 longitudinal patients\u0026apos; cohorts. Mov Disord 34 (12):1839-1850. doi:10.1002/mds.27845\u003c/li\u003e\n\u003cli\u003eCalabresi P, Mechelli A, Natale G, Volpicelli-Daley L, Di Lazzaro G, Ghiglieri V (2023) Alpha-synuclein in Parkinson\u0026apos;s disease and other synucleinopathies: from overt neurodegeneration back to early synaptic dysfunction. Cell Death Dis 14 (3):176. doi:10.1038/s41419-023-05672-9\u003c/li\u003e\n\u003cli\u003eYe H, Robak LA, Yu M, Cykowski M, Shulman JM (2023) Genetics and Pathogenesis of Parkinson\u0026apos;s Syndrome. Annu Rev Pathol 18:95-121. doi:10.1146/annurev-pathmechdis-031521-034145\u003c/li\u003e\n\u003cli\u003eMestre TA, Fereshtehnejad SM, Berg D, Bohnen NI, Dujardin K, Erro R, Espay AJ, Halliday G, van Hilten JJ, Hu MT, Jeon B, Klein C, Leentjens AFG, Marinus J, Mollenhauer B, Postuma R, Rajalingam R, Rodr\u0026iacute;guez-Violante M, Simuni T, Surmeier DJ, Weintraub D, McDermott MP, Lawton M, Marras C (2021) Parkinson\u0026apos;s Disease Subtypes: Critical Appraisal and Recommendations. J Parkinsons Dis 11 (2):395-404. doi:10.3233/jpd-202472\u003c/li\u003e\n\u003cli\u003eKhodadadian A, Hemmati-Dinarvand M, Kalantary-Charvadeh A, Ghobadi A, Mazaheri M (2018) Candidate biomarkers for Parkinson\u0026apos;s disease. Biomed Pharmacother 104:699-704. doi:10.1016/j.biopha.2018.05.026\u003c/li\u003e\n\u003cli\u003eMollenhauer B, Zimmermann J, Sixel-Doering F, Focke NK, Wicke T, Ebentheuer J, Schaumburg M, Lang E, Friede T, Trenkwalder C, DeNoPa Study G (2019) Baseline predictors for progression 4 years after Parkinson\u0026apos;s disease diagnosis in the De Novo Parkinson Cohort (DeNoPa). MOVEMENT DISORDERS 34 (1):67-77. doi:10.1002/mds.27492\u003c/li\u003e\n\u003cli\u003eKoeglsperger T, Rumpf SL, Schlie\u0026szlig;er P, Struebing FL, Brendel M, Levin J, Trenkwalder C, H\u0026ouml;glinger GU, Herms J (2023) Neuropathology of incidental Lewy body \u0026amp; prodromal Parkinson\u0026apos;s disease. Mol Neurodegener 18 (1):32. doi:10.1186/s13024-023-00622-7\u003c/li\u003e\n\u003cli\u003evan de Berg WD, Hepp DH, Dijkstra AA, Rozemuller JA, Berendse HW, Foncke E (2012) Patterns of \u0026alpha;-synuclein pathology in incidental cases and clinical subtypes of Parkinson\u0026apos;s disease. Parkinsonism Relat Disord 18 Suppl 1:S28-30. doi:10.1016/s1353-8020(11)70011-6\u003c/li\u003e\n\u003cli\u003eChang CW, Yang SY, Yang CC, Chang CW, Wu YR (2019) Plasma and Serum Alpha-Synuclein as a Biomarker of Diagnosis in Patients With Parkinson\u0026apos;s Disease. Front Neurol 10:1388. doi:10.3389/fneur.2019.01388\u003c/li\u003e\n\u003cli\u003eFan Z, Pan YT, Zhang ZY, Yang H, Yu SY, Zheng Y, Ma JH, Wang XM (2020) Systemic activation of NLRP3 inflammasome and plasma \u0026alpha;-synuclein levels are correlated with motor severity and progression in Parkinson\u0026apos;s disease. J Neuroinflammation 17 (1):11. doi:10.1186/s12974-019-1670-6\u003c/li\u003e\n\u003cli\u003eChahine LM, Beach TG, Brumm MC, Adler CH, Coffey CS, Mosovsky S, Caspell-Garcia C, Serrano GE, Munoz DG, White CL, 3rd, Crary JF, Jennings D, Taylor P, Foroud T, Arnedo V, Kopil CM, Riley L, Dave KD, Mollenhauer B (2020) In vivo distribution of \u0026alpha;-synuclein in multiple tissues and biofluids in Parkinson disease. Neurology 95 (9):e1267-e1284. doi:10.1212/wnl.0000000000010404\u003c/li\u003e\n\u003cli\u003eSchulz I, Kruse N, Gera RG, Kremer T, Cedarbaum J, Barbour R, Zago W, Schade S, Otte B, Bartl M, Hutten SJ, Trenkwalder C, Mollenhauer B (2021) Systematic Assessment of 10 Biomarker Candidates Focusing on alpha-Synuclein-Related Disorders. MOVEMENT DISORDERS 36 (12):2874-2887. doi:10.1002/mds.28738\u003c/li\u003e\n\u003cli\u003eZubelzu M, Morera-Herreras T, Irastorza G, G\u0026oacute;mez-Esteban JC, Murueta-Goyena A (2022) Plasma and serum alpha-synuclein as a biomarker in Parkinson\u0026apos;s disease: A meta-analysis. Parkinsonism Relat Disord 99:107-115. doi:10.1016/j.parkreldis.2022.06.001\u003c/li\u003e\n\u003cli\u003eZhang Q, Lin Z, He Y, Jiang J, Hu D (2023) Mendelian Randomization Analysis Reveals No Causal Relationship Between Plasma \u0026alpha;-Synuclein and Parkinson\u0026apos;s Disease. Mol Neurobiol 60 (4):2268-2276. doi:10.1007/s12035-023-03206-0\u003c/li\u003e\n\u003cli\u003eWennstr\u0026ouml;m M, Surova Y, Hall S, Nilsson C, Minthon L, Bostr\u0026ouml;m F, Hansson O, Nielsen HM (2013) Low CSF levels of both \u0026alpha;-synuclein and the \u0026alpha;-synuclein cleaving enzyme neurosin in patients with synucleinopathy. PLoS One 8 (1):e53250. doi:10.1371/journal.pone.0053250\u003c/li\u003e\n\u003cli\u003evan Dijk KD, Bidinosti M, Weiss A, Raijmakers P, Berendse HW, van de Berg WD (2014) Reduced \u0026alpha;-synuclein levels in cerebrospinal fluid in Parkinson\u0026apos;s disease are unrelated to clinical and imaging measures of disease severity. Eur J Neurol 21 (3):388-394. doi:10.1111/ene.12176\u003c/li\u003e\n\u003cli\u003eTokuda T, Salem SA, Allsop D, Mizuno T, Nakagawa M, Qureshi MM, Locascio JJ, Schlossmacher MG, El-Agnaf OM (2006) Decreased alpha-synuclein in cerebrospinal fluid of aged individuals and subjects with Parkinson\u0026apos;s disease. Biochem Biophys Res Commun 349 (1):162-166. doi:10.1016/j.bbrc.2006.08.024\u003c/li\u003e\n\u003cli\u003eKang JH, Irwin DJ, Chen-Plotkin AS, Siderowf A, Caspell C, Coffey CS, Walig\u0026oacute;rska T, Taylor P, Pan S, Frasier M, Marek K, Kieburtz K, Jennings D, Simuni T, Tanner CM, Singleton A, Toga AW, Chowdhury S, Mollenhauer B, Trojanowski JQ, Shaw LM (2013) Association of cerebrospinal fluid \u0026beta;-amyloid 1-42, T-tau, P-tau181, and \u0026alpha;-synuclein levels with clinical features of drug-naive patients with early Parkinson disease. JAMA Neurol 70 (10):1277-1287. doi:10.1001/jamaneurol.2013.3861\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Parkinson’s disease, Disease progression, Alpha-synuclein, GPNMB, mendelian randomization, FinnGen","lastPublishedDoi":"10.21203/rs.3.rs-4525984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4525984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe prevalence of Parkinson’s disease (PD) is increasing because of the aging population. Early diagnosis and prognosis of PD remain challenging, suggesting that seeking appropriate biomarkers for PD is crucial. GPNMB and Alpha-synuclein (α-syn) have been reported to contribute to PD pathogenesis and are correlated with PD onset and disease progression. We utilized Mendelian Randomization (MR) analysis to elucidate the association of GPNMB and α-syn with PD and its disease progression. Five MR methods were employed, and inverse variance weighted was chosen as the primary method. The results of MR analysis showed that cerebrospinal fluid (CSF) α-syn correlated with the Unified Parkinson's Disease Rating Scale Ⅲ (UPDRS Ⅲ) and Hoehn and Yahr (H\u0026amp;Y) stage, and plasma α-syn was associated with H\u0026amp;Y stage at baseline suggestively, indicating that α-syn is a promising biomarker for motor symptoms of PD. Overall, CSF and plasma α-syn are potential biomarkers for predicting PD motor symptoms, which warrant further studies. However, no association was detected between GPNMB and PD risk or disease progression.\u003c/p\u003e","manuscriptTitle":"A Mendelian Randomization Study on Association of Alpha-synuclein and GPNMB with Parkinson’s Disease Risk and Progression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-24 12:14:55","doi":"10.21203/rs.3.rs-4525984/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-28T13:27:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-28T02:44:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234653226043839554897082359324042082638","date":"2024-11-18T15:34:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184231363483678508265593188424996828384","date":"2024-11-07T22:30:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-28T20:35:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-28T03:11:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267692000483413861473972349302784753664","date":"2024-10-28T01:10:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158984125672020412447219022722548335895","date":"2024-08-24T06:04:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"71165244855397378201178763630436530660","date":"2024-08-13T04:40:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-12T07:10:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-11T11:14:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-09T23:29:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurobiology","date":"2024-06-04T07:24:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"00ab49d5-11cd-4ac3-b73d-f37b8c5a509c","owner":[],"postedDate":"June 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-14T16:12:33+00:00","versionOfRecord":{"articleIdentity":"rs-4525984","link":"https://doi.org/10.1007/s12035-025-04928-z","journal":{"identity":"molecular-neurobiology","isVorOnly":false,"title":"Molecular Neurobiology"},"publishedOn":"2025-04-11 16:05:18","publishedOnDateReadable":"April 11th, 2025"},"versionCreatedAt":"2024-06-24 12:14:55","video":"","vorDoi":"10.1007/s12035-025-04928-z","vorDoiUrl":"https://doi.org/10.1007/s12035-025-04928-z","workflowStages":[]},"version":"v1","identity":"rs-4525984","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4525984","identity":"rs-4525984","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}