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Trevisan L, Gaudio A, Monfrini E, Avanzino L, Di Fonzo A, Mandich P. Genetics in Parkinson's disease, state-of-the-art and future perspectives. Br Med Bull 2024; 149:60-71. [PMID: 38282031 PMCID: PMC10938543 DOI: 10.1093/bmb/ldad035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 11/30/2023] [Accepted: 12/12/2023] [Indexed: 01/30/2024]
Abstract
BACKGROUND Parkinson's disease (PD) is the second most common neurodegenerative disorder and is clinically characterized by the presence of motor (bradykinesia, rigidity, rest tremor and postural instability) and non-motor symptoms (cognitive impairment, autonomic dysfunction, sleep disorders, depression and hyposmia). The aetiology of PD is unknown except for a small but significant contribution of monogenic forms. SOURCES OF DATA No new data were generated or analyzed in support of this review. AREAS OF AGREEMENT Up to 15% of PD patients carry pathogenic variants in PD-associated genes. Some of these genes are associated with mendelian inheritance, while others act as risk factors. Genetic background influences age of onset, disease course, prognosis and therapeutic response. AREAS OF CONTROVERSY Genetic testing is not routinely offered in the clinical setting, but it may have relevant implications, especially in terms of prognosis, response to therapies and inclusion in clinical trials. Widely adopted clinical guidelines on genetic testing are still lacking and open to debate. Some new genetic associations are still awaiting confirmation, and selecting the appropriate genes to be included in diagnostic panels represents a difficult task. Finally, it is still under study whether (and to which degree) specific genetic forms may influence the outcome of PD therapies. GROWING POINTS Polygenic Risk Scores (PRS) may represent a useful tool to genetically stratify the population in terms of disease risk, prognosis and therapeutic outcomes. AREAS TIMELY FOR DEVELOPING RESEARCH The application of PRS and integrated multi-omics in PD promises to improve the personalized care of patients.
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Affiliation(s)
- L Trevisan
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Largo P. Daneo 3, Genova, 16132, Italy
- IRCCS Ospedale Policlinico San Martino – SS Centro Tumori Ereditari, Largo R. Benzi 10, Genova, 16132, Italy
| | - A Gaudio
- IRCCS Ospedale Policlinico San Martino- UOC Genetica Medica, Largo R. Benzi 10, Genova, 16132, Italy
| | - E Monfrini
- Dino Ferrari Center, Neuroscience Section, Department of Pathophysiology and Transplantation, University of Milan, Via Francesco Sforza 35, Milan, 20122, Italy
- Neurology Unit, Foundation IRCCS Ca’Granda Ospedale Maggiore Policlinico, Via Festa del Perdono 7, Milan, 20122, Italy
| | - L Avanzino
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Viale Benedetto XV/3, Genova, 16132, Italy
- IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 3, Genova, 16132, Italy
| | - A Di Fonzo
- Dino Ferrari Center, Neuroscience Section, Department of Pathophysiology and Transplantation, University of Milan, Via Francesco Sforza 35, Milan, 20122, Italy
- Neurology Unit, Foundation IRCCS Ca’Granda Ospedale Maggiore Policlinico, Via Festa del Perdono 7, Milan, 20122, Italy
| | - P Mandich
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Largo P. Daneo 3, Genova, 16132, Italy
- IRCCS Ospedale Policlinico San Martino- UOC Genetica Medica, Largo R. Benzi 10, Genova, 16132, Italy
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Chung CW, Chou SC, Hsiao TH, Zhang GJ, Chung YF, Chen YM. Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health records. BioData Min 2024; 17:1. [PMID: 38183082 PMCID: PMC10770905 DOI: 10.1186/s13040-023-00352-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 12/19/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND Although the 2019 EULAR/ACR classification criteria for systemic lupus erythematosus (SLE) has required at least a positive anti-nuclear antibody (ANA) titer (≥ 1:80), it remains challenging for clinicians to identify patients with SLE. This study aimed to develop a machine learning (ML) approach to assist in the detection of SLE patients using genomic data and electronic health records. METHODS Participants with a positive ANA (≥ 1:80) were enrolled from the Taiwan Precision Medicine Initiative cohort. The Taiwan Biobank version 2 array was used to detect single nucleotide polymorphism (SNP) data. Six ML models, Logistic Regression, Random Forest (RF), Support Vector Machine, Light Gradient Boosting Machine, Gradient Tree Boosting, and Extreme Gradient Boosting (XGB), were used to identify SLE patients. The importance of the clinical and genetic features was determined by Shapley Additive Explanation (SHAP) values. A logistic regression model was applied to identify genetic variations associated with SLE in the subset of patients with an ANA equal to or exceeding 1:640. RESULTS A total of 946 SLE and 1,892 non-SLE controls were included in this analysis. Among the six ML models, RF and XGB demonstrated superior performance in the differentiation of SLE from non-SLE. The leading features in the SHAP diagram were anti-double strand DNA antibodies, ANA titers, AC4 ANA pattern, polygenic risk scores, complement levels, and SNPs. Additionally, in the subgroup with a high ANA titer (≥ 1:640), six SNPs positively associated with SLE and five SNPs negatively correlated with SLE were discovered. CONCLUSIONS ML approaches offer the potential to assist in diagnosing SLE and uncovering novel SNPs in a group of patients with autoimmunity.
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Affiliation(s)
- Chih-Wei Chung
- Department of Information Management, National Taiwan University, Taipei, Taiwan
| | - Seng-Cho Chou
- Department of Information Management, National Taiwan University, Taipei, Taiwan
| | - Tzu-Hung Hsiao
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
- Department of Public Health, Fu Jen Catholic University, New Taipei City, Taiwan
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan
| | - Grace Joyce Zhang
- Department of Cellular and Physiological Sciences, The University of British Columbia, Vancouver, BC, Canada
| | - Yu-Fang Chung
- Department of Electrical Engineering, Tunghai University, Taichung, Taiwan
| | - Yi-Ming Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.
- Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Taichung Veterans General Hospital, 1650, Section 4, Taiwan Boulevard, Xitun Dist., Taichung City, 407, Taiwan.
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Rong Hsing Research Center for Translational Medicine & Ph.D. Program in Translational Medicine, National Chung Hsing University, Taichung, Taiwan.
- Precision Medicine Research Center, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
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Abstract
The discovery of a pathogenic variant in the alpha-synuclein (SNCA) gene in the Contursi kindred in 1997 indisputably confirmed a genetic cause in a subset of Parkinson's disease (PD) patients. Currently, pathogenic variants in one of the seven established PD genes or the strongest known risk factor gene, GBA1, are identified in ∼15% of PD patients unselected for age at onset and family history. In this Debate article, we highlight multiple avenues of research that suggest an important - and in some cases even predominant - role for genetics in PD aetiology, including familial clustering, high rates of monogenic PD in selected populations, and complete penetrance with certain forms. At first sight, the steep increase in PD prevalence exceeding that of other neurodegenerative diseases may argue against a predominant genetic etiology. Notably, the principal genetic contribution in PD is conferred by pathogenic variants in LRRK2 and GBA1 and, in both cases, characterized by an overall late age of onset and age-related penetrance. In addition, polygenic risk plays a considerable role in PD. However, it is likely that, in the majority of PD patients, a complex interplay of aging, genetic, environmental, and epigenetic factors leads to disease development.
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Affiliation(s)
- Shen-Yang Lim
- The Mah Pooi Soo and Tan Chin Nam Centre for Parkinson's and Related Disorders, University of Malaya, Kuala Lumpur, Malaysia
- Department of Medicine, Faculty of Medicine, Division of Neurology, University of Malaya, Kuala Lumpur, Malaysia
| | - Christine Klein
- Institute of Neurogenetics, University of Luebeck, Luebeck, Germany
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Landoulsi Z, Pachchek S, Bobbili DR, Pavelka L, May P, Krüger R. Genetic landscape of Parkinson's disease and related diseases in Luxembourg. Front Aging Neurosci 2023; 15:1282174. [PMID: 38173558 PMCID: PMC10761438 DOI: 10.3389/fnagi.2023.1282174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
Objectives To explore the genetic architecture of PD in the Luxembourg Parkinson's Study including cohorts of healthy people and patients with Parkinson's disease (PD) and atypical parkinsonism (AP). Methods 809 healthy controls, 680 PD and 103 AP were genotyped using the Neurochip array. We screened and validated rare single nucleotide variants (SNVs) and copy number variants (CNVs) within seven PD-causing genes (LRRK2, SNCA, VPS35, PRKN, PARK7, PINK1 and ATP13A2). Polygenic risk scores (PRSs) were generated using the latest genome-wide association study for PD. We then estimated the role of common variants in PD risk by applying gene-set-specific PRSs. Results We identified 60 rare SNVs in seven PD-causing genes, nine of which were pathogenic in LRRK2, PINK1 and PRKN. Eleven rare CNVs were detected in PRKN including seven duplications and four deletions. The majority of PRKN SNVs and CNVs carriers were heterozygous and not differentially distributed between cases and controls. The PRSs were significantly associated with PD and identified specific molecular pathways related to protein metabolism and signal transduction as drivers of PD risk. Conclusion We performed a comprehensive genetic characterization of the deep-phenotyped individuals of the Luxembourgish Parkinson's Study. Heterozygous SNVs and CNVs in PRKN were not associated with higher PD risk. In particular, we reported novel digenic variants in PD related genes and rare LRRK2 SNVs in AP patients. Our findings will help future studies to unravel the genetic complexity of PD.
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Affiliation(s)
- Zied Landoulsi
- LCSB, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Sinthuja Pachchek
- LCSB, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Dheeraj Reddy Bobbili
- LCSB, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Lukas Pavelka
- Parkinson Research Clinic, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Patrick May
- LCSB, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Rejko Krüger
- LCSB, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Parkinson Research Clinic, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health (LIH), Strassen, Luxembourg
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Xu L, Zhou G, Jiang W, Guan L, Zhao H. Leveraging genetic correlations and multiple populations to improve genetic risk prediction for non-European populations. bioRxiv 2023:2023.10.29.564615. [PMID: 37961111 PMCID: PMC10634936 DOI: 10.1101/2023.10.29.564615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The disparity in genetic risk prediction accuracy between European and non-European individuals highlights a critical challenge in health inequality. To bridge this gap, we introduce JointPRS, a novel method that models multiple populations jointly to improve genetic risk predictions for non-European individuals. JointPRS has three key features. First, it encompasses all diverse populations to improve prediction accuracy, rather than relying solely on the target population with a singular auxiliary European group. Second, it autonomously estimates and leverages chromosome-wise cross-population genetic correlations to infer the effect sizes of genetic variants. Lastly, it provides an auto version that has comparable performance to the tuning version to accommodate the situation with no validation dataset. Through extensive simulations and real data applications to 22 quantitative traits and four binary traits in East Asian populations, nine quantitative traits and one binary trait in African populations, and four quantitative traits in South Asian populations, we demonstrate that JointPRS outperforms state-of-art methods, improving the prediction accuracy for both quantitative and binary traits in non-European populations.
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Affiliation(s)
- Leqi Xu
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Geyu Zhou
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Wei Jiang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Leying Guan
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
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Wolff A, Schumacher NU, Pürner D, Machetanz G, Demleitner AF, Feneberg E, Hagemeier M, Lingor P. Parkinson's disease therapy: what lies ahead? J Neural Transm (Vienna) 2023; 130:793-820. [PMID: 37147404 PMCID: PMC10199869 DOI: 10.1007/s00702-023-02641-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 04/25/2023] [Indexed: 05/07/2023]
Abstract
The worldwide prevalence of Parkinson's disease (PD) has been constantly increasing in the last decades. With rising life expectancy, a longer disease duration in PD patients is observed, further increasing the need and socioeconomic importance of adequate PD treatment. Today, PD is exclusively treated symptomatically, mainly by dopaminergic stimulation, while efforts to modify disease progression could not yet be translated to the clinics. New formulations of approved drugs and treatment options of motor fluctuations in advanced stages accompanied by telehealth monitoring have improved PD patients care. In addition, continuous improvement in the understanding of PD disease mechanisms resulted in the identification of new pharmacological targets. Applying novel trial designs, targeting of pre-symptomatic disease stages, and the acknowledgment of PD heterogeneity raise hopes to overcome past failures in the development of drugs for disease modification. In this review, we address these recent developments and venture a glimpse into the future of PD therapy in the years to come.
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Affiliation(s)
- Andreas Wolff
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Nicolas U Schumacher
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Dominik Pürner
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Gerrit Machetanz
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Antonia F Demleitner
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Emily Feneberg
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Maike Hagemeier
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany
| | - Paul Lingor
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
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Gordon RL, Martschenko DO, Nayak S, Niarchou M, Morrison MD, Bell E, Jacoby N, Davis LK. Confronting ethical and social issues related to the genetics of musicality. Ann N Y Acad Sci 2023; 1522:5-14. [PMID: 36851882 PMCID: PMC10613828 DOI: 10.1111/nyas.14972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
New interdisciplinary research into genetic influences on musicality raises a number of ethical and social issues for future avenues of research and public engagement. The historical intersection of music cognition and eugenics heightens the need to vigilantly weigh the potential risks and benefits of these studies and the use of their outcomes. Here, we bring together diverse disciplinary expertise (complex trait genetics, music cognition, musicology, bioethics, developmental psychology, and neuroscience) to interpret and guide the ethical use of findings from recent and future studies. We discuss a framework for incorporating principles of ethically and socially responsible conduct of musicality genetics research into each stage of the research lifecycle: study design, study implementation, potential applications, and communication.
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Affiliation(s)
- Reyna L. Gordon
- Department of Otolaryngology- Head & Neck Surgery, Vanderbilt University Medical Center, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, USA
| | | | - Srishti Nayak
- Department of Otolaryngology- Head & Neck Surgery, Vanderbilt University Medical Center, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, USA
| | - Maria Niarchou
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, TN, USA
| | - Matthew D. Morrison
- Clive Davis Institute of Recorded Music, Tisch School of the Arts, New York University, New York, NY, USA
| | - Eamonn Bell
- Department of Music/Graduate School of Arts and Sciences, Columbia University, New York, NY, USA
- Department of Computer Science, Durham University, Durham, United Kingdom
| | - Nori Jacoby
- Computational Auditory Perception Research Group, Max Planck Institute for Empirical Aesthetics, Frankfurt, Germany
| | - Lea K. Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, TN, USA
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Šetinc M, Celinšćak Ž, Bočkor L, Ćorić T, Kolarić B, Stojanović Marković A, Zajc Petranović M, Peričić Salihović M, Smolej Narančić N, Škarić-Jurić T. Genetic scores for predicting longevity in the Croatian oldest-old population. PLoS One 2023; 18:e0279971. [PMID: 36735720 DOI: 10.1371/journal.pone.0279971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/19/2022] [Indexed: 02/04/2023] Open
Abstract
Longevity is a hallmark of successful ageing and a complex trait with a significant genetic component. In this study, 43 single nucleotide polymorphisms (SNPs) were chosen from the literature and genotyped in a Croatian oldest-old sample (85+ years, sample size (N) = 314), in order to determine whether any of these SNPs have a significant effect on reaching the age thresholds for longevity (90+ years, N = 212) and extreme longevity (95+ years, N = 84). The best models were selected for both survival ages using multivariate logistic regression. In the model for reaching age 90, nine SNPs explained 20% of variance for survival to that age, while the 95-year model included five SNPs accounting for 9.3% of variance. The two SNPs that showed the most significant association (p ≤ 0.01) with longevity were TERC rs16847897 and GHRHR rs2267723. Unweighted and weighted Genetic Longevity Scores (uGLS and wGLS) were calculated and their predictive power was tested. All four scores showed significant correlation with age at death (p ≤ 0.01). They also passed the ROC curve test with at least 50% predictive ability, but wGLS90 stood out as the most accurate score, with a 69% chance of accurately predicting survival to the age of 90.
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Höllerhage M, Klietz M, Höglinger GU. Disease modification in Parkinsonism: obstacles and ways forward. J Neural Transm (Vienna) 2022; 129:1133-1153. [PMID: 35695938 PMCID: PMC9463344 DOI: 10.1007/s00702-022-02520-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/21/2022] [Indexed: 12/19/2022]
Abstract
To date, the diagnoses of Parkinson syndromes are based on clinical examination. Therefore, these specific diagnoses are made, when the neuropathological process is already advanced. However, disease modification or neuroprotection, is considered to be most effective before marked neurodegeneration has occurred. In recent years, early clinical or prodromal stages of Parkinson syndromes came into focus. Moreover, subtypes of distinct diseases will allow predictions of the individual course of the diseases more precisely. Thereby, patients will be enrolled into clinical trials with more specific disease entities and endpoints. Furthermore, novel fluid and imaging biomarkers that allow biochemical diagnoses are under development. These will lead to earlier diagnoses and earlier therapy in the future as consequence. Furthermore, therapeutic approaches will take the underlying neuropathological process of neurodegenerative Parkinson syndromes more specific into account. Specifically, future therapies will target the aggregation of aggregation-prone proteins such as alpha-synuclein and tau, the degradation of pathological aggregates, and the spreading of pathological protein aggregates throughout the brain. Many of these approaches are already in (pre)clinical development. In addition, anti-inflammatory approaches are in development. Furthermore, drug-repurposing is a feasible approach to shorten the developmental process of new drugs.
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Affiliation(s)
- M Höllerhage
- Department of Neurology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - M Klietz
- Department of Neurology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - G U Höglinger
- Department of Neurology, Hannover Medical School, Carl-Neuberg-Str. 1, 30625, Hannover, Germany.
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Redenšek S, Kristanc T, Blagus T, Trošt M, Dolžan V. Genetic Variability of the Vitamin D Receptor Affects Susceptibility to Parkinson’s Disease and Dopaminergic Treatment Adverse Events. Front Aging Neurosci 2022; 14:853277. [PMID: 35517045 PMCID: PMC9063754 DOI: 10.3389/fnagi.2022.853277] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 03/11/2022] [Indexed: 11/13/2022] Open
Abstract
Vitamin D is a lipid-soluble molecule and an important transcriptional regulator in many tissues and organs, including the brain. Its role has been demonstrated also in Parkinson’s disease (PD) pathogenesis. Vitamin D receptor (VDR) is responsible for the initiation of vitamin D signaling cascade. The aim of this study was to assess the associations of VDR genetic variability with PD risk and different PD-related phenotypes. We genotyped 231 well characterized PD patients and 161 healthy blood donors for six VDR single nucleotide polymorphisms, namely rs739837, rs4516035, rs11568820, rs731236, rs2228570, and rs1544410. We observed that VDR rs2228570 is associated with PD risk (p < 0.001). Additionally, we observed associations of specific VDR genotypes with adverse events of dopaminergic treatment. VDR rs1544410 (GG vs. GA + AA: p = 0.005; GG vs. GA: p = 0.009) was associated with the occurrence of visual hallucinations and VDR rs739837 (TT vs. GG: p = 0.036), rs731236 (TT vs. TC + CC: p = 0.011; TT vs. TC: p = 0.028; TT vs. CC: p = 0.035), and rs1544410 (GG vs. GA: p = 0.014) with the occurrence of orthostatic hypotension. We believe that the reported study may support personalized approach to PD treatment, especially in terms of monitoring vitamin D level and vitamin D supplementation in patients with high risk VDR genotypes.
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Affiliation(s)
- Sara Redenšek
- Pharmacogenetics Laboratory, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Tilen Kristanc
- Pharmacogenetics Laboratory, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Tanja Blagus
- Pharmacogenetics Laboratory, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Maja Trošt
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, Slovenia
| | - Vita Dolžan
- Pharmacogenetics Laboratory, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- *Correspondence: Vita Dolžan,
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