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Reynoso A, Torricelli R, Jacobs BM, Shi J, Aslibekyan S, Norcliffe-Kaufmann L, Noyce AJ, Heilbron K. Gene-Environment Interactions for Parkinson's Disease. Ann Neurol 2024; 95:677-687. [PMID: 38113326 DOI: 10.1002/ana.26852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 12/06/2023] [Accepted: 12/06/2023] [Indexed: 12/21/2023]
Abstract
OBJECTIVE Parkinson's disease (PD) is a neurodegenerative disorder with complex etiology. Multiple genetic and environmental factors have been associated with PD, but most PD risk remains unexplained. The aim of this study was to test for statistical interactions between PD-related genetic and environmental exposures in the 23andMe, Inc. research dataset. METHODS Using a validated PD polygenic risk score and common PD-associated variants in the GBA gene, we explored interactions between genetic susceptibility factors and 7 lifestyle and environmental factors: body mass index (BMI), type 2 diabetes (T2D), tobacco use, caffeine consumption, pesticide exposure, head injury, and physical activity (PA). RESULTS We observed that T2D, as well as higher BMI, caffeine consumption, and tobacco use, were associated with lower odds of PD, whereas head injury, pesticide exposure, GBA carrier status, and PD polygenic risk score were associated with higher odds. No significant association was observed between PA and PD. In interaction analyses, we found statistical evidence for an interaction between polygenic risk of PD and the following environmental/lifestyle factors: T2D (p = 6.502 × 10-8), PA (p = 8.745 × 10-5), BMI (p = 4.314 × 10-4), and tobacco use (p = 2.236 × 10-3). Although BMI and tobacco use were associated with lower odds of PD regardless of the extent of individual genetic liability, the direction of the relationship between odds of PD and T2D, as well as PD and PA, varied depending on polygenic risk score. INTERPRETATION We provide preliminary evidence that associations between some environmental and lifestyle factors and PD may be modified by genotype. ANN NEUROL 2024;95:677-687.
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Affiliation(s)
| | - Roberta Torricelli
- Center for Preventive Neurology, Wolfson Institute of Population Health, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Benjamin Meir Jacobs
- Center for Preventive Neurology, Wolfson Institute of Population Health, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
| | | | | | | | - Alastair J Noyce
- Center for Preventive Neurology, Wolfson Institute of Population Health, Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Karl Heilbron
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin, Berlin, Germany
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
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2
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Leonard HL, Nalls MA, Day-Williams A, Esmaeeli S, Jarreau P, Bandres-Ciga S, Heutink P, Sardi SP, Singleton AB. Open science in precision medicine for neurodegenerative diseases. Nat Rev Drug Discov 2024; 23:233-234. [PMID: 38287165 DOI: 10.1038/d41573-024-00017-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
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3
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Bhalala OG, Watson R, Yassi N. Multi-Omic Blood Biomarkers as Dynamic Risk Predictors in Late-Onset Alzheimer's Disease. Int J Mol Sci 2024; 25:1231. [PMID: 38279230 PMCID: PMC10816901 DOI: 10.3390/ijms25021231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 01/28/2024] Open
Abstract
Late-onset Alzheimer's disease is the leading cause of dementia worldwide, accounting for a growing burden of morbidity and mortality. Diagnosing Alzheimer's disease before symptoms are established is clinically challenging, but would provide therapeutic windows for disease-modifying interventions. Blood biomarkers, including genetics, proteins and metabolites, are emerging as powerful predictors of Alzheimer's disease at various timepoints within the disease course, including at the preclinical stage. In this review, we discuss recent advances in such blood biomarkers for determining disease risk. We highlight how leveraging polygenic risk scores, based on genome-wide association studies, can help stratify individuals along their risk profile. We summarize studies analyzing protein biomarkers, as well as report on recent proteomic- and metabolomic-based prediction models. Finally, we discuss how a combination of multi-omic blood biomarkers can potentially be used in memory clinics for diagnosis and to assess the dynamic risk an individual has for developing Alzheimer's disease dementia.
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Affiliation(s)
- Oneil G. Bhalala
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
| | - Rosie Watson
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
| | - Nawaf Yassi
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
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4
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Simona A, Song W, Bates DW, Samer CF. Polygenic risk scores in pharmacogenomics: opportunities and challenges-a mini review. Front Genet 2023; 14:1217049. [PMID: 37396043 PMCID: PMC10311496 DOI: 10.3389/fgene.2023.1217049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/08/2023] [Indexed: 07/04/2023] Open
Abstract
Pharmacogenomics (PGx) aims at tailoring drug therapy by considering patient genetic makeup. While drug dosage guidelines have been extensively based on single gene mutations (single nucleotide polymorphisms) over the last decade, polygenic risk scores (PRS) have emerged in the past years as a promising tool to account for the complex interplay and polygenic nature of patients' genetic predisposition affecting drug response. Even though PRS research has demonstrated convincing evidence in disease risk prediction, the clinical utility and its implementation in daily care has yet to be demonstrated, and pharmacogenomics is no exception; usual endpoints include drug efficacy or toxicity. Here, we review the general pipeline in PRS calculation, and we discuss some of the remaining barriers and challenges that must be undertaken to bring PRS research in PGx closer to patient care. Besides the need in following reporting guidelines and larger PGx patient cohorts, PRS integration will require close collaboration between bioinformatician, treating physicians and genetic consultants to ensure a transparent, generalizable, and trustful implementation of PRS results in real-world medical decisions.
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Affiliation(s)
- Aurélien Simona
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
- Division of General Internal Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Wenyu Song
- Division of General Internal Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - David W. Bates
- Division of General Internal Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Department of Health Policy and Management, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Caroline Flora Samer
- Division of Clinical Pharmacology and Toxicology, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
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5
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Levine KS, Leonard HL, Blauwendraat C, Iwaki H, Johnson N, Bandres-Ciga S, Ferrucci L, Faghri F, Singleton AB, Nalls MA. Virus exposure and neurodegenerative disease risk across national biobanks. Neuron 2023; 111:1086-1093.e2. [PMID: 36669485 PMCID: PMC10079561 DOI: 10.1016/j.neuron.2022.12.029] [Citation(s) in RCA: 83] [Impact Index Per Article: 83.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/07/2022] [Accepted: 12/22/2022] [Indexed: 01/20/2023]
Abstract
With recent findings connecting the Epstein-Barr virus to an increased risk of multiple sclerosis and growing concerns regarding the neurological impact of the coronavirus pandemic, we examined potential links between viral exposures and neurodegenerative disease risk. Using time series data from FinnGen for discovery and cross-sectional data from the UK Biobank for replication, we identified 45 viral exposures significantly associated with increased risk of neurodegenerative disease and replicated 22 of these associations. The largest effect association was between viral encephalitis exposure and Alzheimer's disease. Influenza with pneumonia was significantly associated with five of the six neurodegenerative diseases studied. We also replicated the Epstein-Barr/multiple sclerosis association. Some of these exposures were associated with an increased risk of neurodegeneration up to 15 years after infection. As vaccines are currently available for some of the associated viruses, vaccination may be a way to reduce some risk of neurodegenerative disease.
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Affiliation(s)
- Kristin S Levine
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Data Tecnica International LLC, Washington DC, USA
| | - Hampton L Leonard
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Data Tecnica International LLC, Washington DC, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA; University of Tuebingen, Tuebingen, Germany
| | - Cornelis Blauwendraat
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Hirotaka Iwaki
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Data Tecnica International LLC, Washington DC, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Nicholas Johnson
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Data Tecnica International LLC, Washington DC, USA
| | - Sara Bandres-Ciga
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Faraz Faghri
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Data Tecnica International LLC, Washington DC, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Andrew B Singleton
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Mike A Nalls
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Data Tecnica International LLC, Washington DC, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
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6
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Mata I, Salles P, Cornejo-Olivas M, Saffie P, Ross OA, Reed X, Bandres-Ciga S. LRRK2: Genetic mechanisms vs genetic subtypes. HANDBOOK OF CLINICAL NEUROLOGY 2023; 193:133-154. [PMID: 36803807 DOI: 10.1016/b978-0-323-85555-6.00018-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
In 2004, the identification of pathogenic variants in the LRRK2 gene across several families with autosomal dominant late-onset Parkinson's disease (PD) revolutionized our understanding of the role of genetics in PD. Previous beliefs that genetics in PD was limited to rare early-onset or familial forms of the disease were quickly dispelled. Currently, we recognize LRRK2 p.G2019S as the most common genetic cause of both sporadic and familial PD, with more than 100,000 affected carriers across the globe. The frequency of LRRK2 p.G2019S is also highly variable across populations, with some regions of Asian or Latin America reporting close to 0%, contrasting to Ashkenazi Jews or North African Berbers reporting up to 13% and 40%, respectively. Patients with LRRK2 pathogenic variants are clinically and pathologically heterogeneous, highlighting the age-related variable penetrance that also characterizes LRRK2-related disease. Indeed, the majority of patients with LRRK2-related disease are characterized by a relatively mild Parkinsonism with less motor symptoms with variable presence of α-synuclein and/or tau aggregates, with pathologic pleomorphism widely described. At a functional cellular level, it is likely that pathogenic variants mediate a toxic gain-of-function of the LRRK2 protein resulting in increased kinase activity perhaps in a cell-specific manner; by contrast, some LRRK2 variants appear to be protective reducing PD risk by decreasing the kinase activity. Therefore, employing this information to define appropriate patient populations for clinical trials of targeted kinase LRRK2 inhibition strategies is very promising and demonstrates a potential future application for PD using precision medicine.
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Affiliation(s)
- Ignacio Mata
- Genomic Medicine Institute (GMI), Cleveland Clinic, Cleveland, OH, United States.
| | - Philippe Salles
- Corporación Centro de Trastornos del Movimiento (CETRAM), Lo Espejo, Santiago, Chile
| | - Mario Cornejo-Olivas
- Neurogenetics Research Center, Instituto Nacional de Ciencias Neurológicas, Lima, Peru
| | - Paula Saffie
- Corporación Centro de Trastornos del Movimiento (CETRAM), Lo Espejo, Santiago, Chile
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, United States
| | - Xylena Reed
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
| | - Sara Bandres-Ciga
- Laboratory of Neurogenetics and Center for Alzheimer's and Related Dementias, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
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7
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Senkevich K, Rudakou U, Gan-Or Z. Genetic mechanism vs genetic subtypes: The example of GBA. HANDBOOK OF CLINICAL NEUROLOGY 2023; 193:155-170. [PMID: 36803808 DOI: 10.1016/b978-0-323-85555-6.00016-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Genetic variants in GBA, encoding the lysosomal enzyme glucocerebrosidase (GCase), are common risk factors for Parkinson's disease (PD). Genotype-phenotype studies have demonstrated that different types of GBA variants have differential effects on the phenotype. Variants could be classified as mild or severe depending on the type of Gaucher disease they cause in the biallelic state. It was shown that severe GBA variants, as compared to mild variants, are associated with higher risk of PD, earlier age at onset, and faster progression of motor and nonmotor symptoms. The observed difference in phenotype might be caused by a diversity of cellular mechanisms related to the particular variants. The lysosomal function of GCase is thought to play a significant role in the development of GBA-associated PD, and other mechanisms such as endoplasmic reticulum retention, mitochondrial dysfunction, and neuroinflammation have also been suggested. Moreover, genetic modifiers such as LRRK2, TMEM175, SNCA, and CTSB can either affect GCase activity or modulate risk and age at onset of GBA-associated PD. To achieve ideal outcomes with precision medicine, therapies will have to be tailored to individuals with specific variants, potentially in combination with known modifiers.
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Affiliation(s)
- Konstantin Senkevich
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montréal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada
| | - Uladzislau Rudakou
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montréal, QC, Canada; Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Ziv Gan-Or
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montréal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada; Department of Human Genetics, McGill University, Montréal, QC, Canada.
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8
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Exploring the Sensitivity of Prodromal Dementia with Lewy Bodies Research Criteria. Brain Sci 2022; 12:brainsci12121594. [PMID: 36552054 PMCID: PMC9775171 DOI: 10.3390/brainsci12121594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/18/2022] [Accepted: 11/20/2022] [Indexed: 11/23/2022] Open
Abstract
Dementia with Lewy bodies (DLB) is an insidious neurodegenerative disease characterised by a precipitous decline in cognition, sleep disturbances, motor impairment and psychiatric features. Recently, criteria for prodromal DLB (pDLB) including clinical features and biomarkers have been put forward to aid the classification and research of this ambiguous cohort of patients. Researchers can use these criteria to classify patients with mild cognitive impairment (MCI) with Lewy bodies (MCI-LB) as either possible (either one core clinical feature or one biomarker are present) or probable pDLB (at least two core clinical features, or one core clinical feature and at least one biomarker present). However, as isolated REM sleep behaviour disorder (iRBD) confirmed with polysomnography (PSG) can be included as both a clinical and a biomarker feature, potentially reducing the specificity of these diagnostic criteria. To address this issue, the current study classified a cohort of 47 PSG-confirmed iRBD patients as probable prodromal DLB only in the presence of an additional core feature or if there was an additional non-PSG biomarker. Thirteen iRBD patients demonstrated MCI (iRBD-MCI). In the iRBD-MCI group, one presented with parkinsonism and was thus classified as probable pDLB, whilst the remaining 12 were classified as only possible pDLB. All patients performed three tasks designed to measure attentional deficits, visual hallucinations and visuospatial impairment. Patients also attended clinical follow-ups to monitor for transition to DLB or another synucleinopathy. Findings indicated that the only patient categorised by virtue of having two core clinical features as probable pDLB transitioned over 28 months to a diagnosis of DLB. The performance of this probable pDLB patient was also ranked second-highest for their hallucinatory behaviours and had comparatively lower visuospatial accuracy. These findings highlight the need for more stringent diagnostic research criteria for pDLB, given that only one of the 13 patients who would have satisfied the current guidelines for probable pDLB transitioned to DLB after two years and was indeed the patient with two orthogonal core clinical features.
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Niotis K, West AB, Saunders-Pullman R. Who to Enroll in Parkinson Disease Prevention Trials? The Case for Genetically At-Risk Cohorts. Neurology 2022; 99:10-18. [PMID: 35970585 DOI: 10.1212/wnl.0000000000200812] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 04/15/2022] [Indexed: 12/15/2022] Open
Abstract
Therapies that prevent the occurrence of Parkinson disease (PD) (primary prevention) or mitigate the progression of symptoms in those with early disease (secondary prevention) are a critical unmet need in disease management. Despite great promise, PD prevention trials have not yet demonstrated success. Initiation of treatment too late in the disease course and the heterogeneity of disease are obstacles that may have contributed to the failure. Genetically stratified groups offer many advantages to primary and secondary prevention trials. In addition to their ease of identification, they decrease disease heterogeneity on several levels. Particularly, they comprise a phenotypically and pathologically enriched group with defined clinical features, pathogenic mechanisms and associated proteins that may serve as specific trial endpoints, therapeutic targets and biomarkers for disease state, and pharmacodynamic and pharmacokinetic status. However, challenges arise from genetic variant heterogeneity, from reduced penetrance whereby many carriers will not develop PD, and in recruiting a population that will meet the desired outcome in the proposed study duration. In this review, we discussed the opportunities afforded by the enrollment of genetically stratified cohorts (i.e., leucine-rich repeat kinase 2 and glucocerebrosidase 1) into prevention trials with a primary focus on primary prevention trials. We also outlined challenges surrounding the enrollment of these cohorts and offered suggestions to leverage their many advantages.
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Affiliation(s)
- Kellyann Niotis
- From the Department of Neurology (K.N., R.S.-P.), Mount Sinai Beth Israel Medical Center; Department of Neurology (K.N., R.S.-P.), Icahn School of Medicine at Mount Sinai, New York; and Duke Center for Neurodegeneration Research (A.B.W.), Departments of Pharmacology and Cancer Biology, Neurology, and Neurobiology, Duke University, Durham, NC
| | - Andrew B West
- From the Department of Neurology (K.N., R.S.-P.), Mount Sinai Beth Israel Medical Center; Department of Neurology (K.N., R.S.-P.), Icahn School of Medicine at Mount Sinai, New York; and Duke Center for Neurodegeneration Research (A.B.W.), Departments of Pharmacology and Cancer Biology, Neurology, and Neurobiology, Duke University, Durham, NC
| | - Rachel Saunders-Pullman
- From the Department of Neurology (K.N., R.S.-P.), Mount Sinai Beth Israel Medical Center; Department of Neurology (K.N., R.S.-P.), Icahn School of Medicine at Mount Sinai, New York; and Duke Center for Neurodegeneration Research (A.B.W.), Departments of Pharmacology and Cancer Biology, Neurology, and Neurobiology, Duke University, Durham, NC.
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Vijiaratnam N, Lawton M, Heslegrave AJ, Guo T, Tan M, Jabbari E, Real R, Woodside J, Grosset K, Chelban V, Athauda D, Girges C, Barker RA, Hardy J, Wood N, Houlden H, Williams N, Ben-Shlomo Y, Zetterberg H, Grosset DG, Foltynie T, Morris HR. Combining biomarkers for prognostic modelling of Parkinson's disease. J Neurol Neurosurg Psychiatry 2022; 93:jnnp-2021-328365. [PMID: 35577512 PMCID: PMC9279845 DOI: 10.1136/jnnp-2021-328365] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/14/2022] [Indexed: 12/11/2022]
Abstract
BACKGROUND Patients with Parkinson's disease (PD) have variable rates of progression. More accurate prediction of progression could improve selection for clinical trials. Although some variance in clinical progression can be predicted by age at onset and phenotype, we hypothesise that this can be further improved by blood biomarkers. OBJECTIVE To determine if blood biomarkers (serum neurofilament light (NfL) and genetic status (glucocerebrosidase, GBA and apolipoprotein E (APOE))) are useful in addition to clinical measures for prognostic modelling in PD. METHODS We evaluated the relationship between serum NfL and baseline and longitudinal clinical measures as well as patients' genetic (GBA and APOE) status. We classified patients as having a favourable or an unfavourable outcome based on a previously validated model, and explored how blood biomarkers compared with clinical variables in distinguishing prognostic phenotypes . RESULTS 291 patients were assessed in this study. Baseline serum NfL was associated with baseline cognitive status. Nfl predicted a shorter time to dementia, postural instability and death (dementia-HR 2.64; postural instability-HR 1.32; mortality-HR 1.89) whereas APOEe4 status was associated with progression to dementia (dementia-HR 3.12, 95% CI 1.63 to 6.00). NfL levels and genetic variables predicted unfavourable progression to a similar extent as clinical predictors. The combination of clinical, NfL and genetic data produced a stronger prediction of unfavourable outcomes compared with age and gender (area under the curve: 0.74-age/gender vs 0.84-ALL p=0.0103). CONCLUSIONS Clinical trials of disease-modifying therapies might usefully stratify patients using clinical, genetic and NfL status at the time of recruitment.
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Affiliation(s)
- Nirosen Vijiaratnam
- Department of Clinical and Movement Neurosciences, University College London, UCL Queen Square Institute of Neurology, London, UK
| | - Michael Lawton
- Population Health Sciences, University of Bristol, Bristol, UK
- Department of Social Medicine, University of Bristol, Bristol, UK
| | - Amanda J Heslegrave
- Dementia Research Institute, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
| | - Tong Guo
- Dementia Research Institute, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
| | - Manuela Tan
- Department of Clinical and Movement Neurosciences, University College London, UCL Queen Square Institute of Neurology, London, UK
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Edwin Jabbari
- Department of Clinical and Movement Neurosciences, University College London, UCL Queen Square Institute of Neurology, London, UK
| | - Raquel Real
- Department of Clinical and Movement Neurosciences, University College London, UCL Queen Square Institute of Neurology, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815
| | - John Woodside
- Department of Clinical and Movement Neurosciences, University College London, UCL Queen Square Institute of Neurology, London, UK
| | - Katherine Grosset
- Department of Neurology, Southern General Hospital, University of Glasgow and Institute of Neurological Sciences, Glasgow, UK
| | - Viorica Chelban
- Department of Clinical and Movement Neurosciences, University College London, UCL Queen Square Institute of Neurology, London, UK
| | - Dilan Athauda
- Department of Clinical and Movement Neurosciences, University College London, UCL Queen Square Institute of Neurology, London, UK
| | - Christine Girges
- Department of Clinical and Movement Neurosciences, University College London, UCL Queen Square Institute of Neurology, London, UK
| | - Roger A Barker
- Cambridge Centre for Brain Repair, University of Cambridge, Cambridge, UK
| | - John Hardy
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815
- Molecular Neuroscience, University College London Institute of Neurology, London, UK
| | - Nicholas Wood
- Department of Clinical and Movement Neurosciences, University College London, UCL Queen Square Institute of Neurology, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815
| | - Henry Houlden
- MRC Centre for Neuromuscular Diseases, UCL Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, UK
| | - Nigel Williams
- Cardiff University, Cardiff University Institute of Psychological Medicine and Clinical Neurosciences, Cardiff, UK
| | - Yoav Ben-Shlomo
- Department of Social Medicine, University of Bristol, Bristol, UK
| | - Henrik Zetterberg
- Dementia Research Institute, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Hong Kong Center, for Neurodegenerative Diseases, Hong Kong, People's Republic of China
| | - Donald G Grosset
- Department of Neurology, Southern General Hospital, University of Glasgow and Institute of Neurological Sciences, Glasgow, UK
| | - Thomas Foltynie
- Department of Clinical and Movement Neurosciences, University College London, UCL Queen Square Institute of Neurology, London, UK
| | - Huw R Morris
- Department of Clinical and Movement Neurosciences, University College London, UCL Queen Square Institute of Neurology, London, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815
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11
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Faghri F, Brunn F, Dadu A, Zucchi E, Martinelli I, Mazzini L, Vasta R, Canosa A, Moglia C, Calvo A, Nalls MA, Campbell RH, Mandrioli J, Traynor BJ, Chiò A. Identifying and predicting amyotrophic lateral sclerosis clinical subgroups: a population-based machine-learning study. Lancet Digit Health 2022; 4:e359-e369. [PMID: 35341712 PMCID: PMC9038712 DOI: 10.1016/s2589-7500(21)00274-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/17/2021] [Accepted: 11/26/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Amyotrophic lateral sclerosis (ALS) is known to represent a collection of overlapping syndromes. Various classification systems based on empirical observations have been proposed, but it is unclear to what extent they reflect ALS population substructures. We aimed to use machine-learning techniques to identify the number and nature of ALS subtypes to obtain a better understanding of this heterogeneity, enhance our understanding of the disease, and improve clinical care. METHODS In this retrospective study, we applied unsupervised Uniform Manifold Approximation and Projection [UMAP]) modelling, semi-supervised (neural network UMAP) modelling, and supervised (ensemble learning based on LightGBM) modelling to a population-based discovery cohort of patients who were diagnosed with ALS while living in the Piedmont and Valle d'Aosta regions of Italy, for whom detailed clinical data, such as age at symptom onset, were available. We excluded patients with missing Revised ALS Functional Rating Scale (ALSFRS-R) feature values from the unsupervised and semi-supervised steps. We replicated our findings in an independent population-based cohort of patients who were diagnosed with ALS while living in the Emilia Romagna region of Italy. FINDINGS Between Jan 1, 1995, and Dec 31, 2015, 2858 patients were entered in the discovery cohort. After excluding 497 (17%) patients with missing ALSFRS-R feature values, data for 42 clinical features across 2361 (83%) patients were available for the unsupervised and semi-supervised analysis. We found that semi-supervised machine learning produced the optimum clustering of the patients with ALS. These clusters roughly corresponded to the six clinical subtypes defined by the Chiò classification system (ie, bulbar, respiratory, flail arm, classical, pyramidal, and flail leg ALS). Between Jan 1, 2009, and March 1, 2018, 1097 patients were entered in the replication cohort. After excluding 108 (10%) patients with missing ALSFRS-R feature values, data for 42 clinical features across 989 patients were available for the unsupervised and semi-supervised analysis. All 1097 patients were included in the supervised analysis. The same clusters were identified in the replication cohort. By contrast, other ALS classification schemes, such as the El Escorial categories, Milano-Torino clinical staging, and King's clinical stages, did not adequately label the clusters. Supervised learning identified 11 clinical parameters that predicted ALS clinical subtypes with high accuracy (area under the curve 0·982 [95% CI 0·980-0·983]). INTERPRETATION Our data-driven study provides insight into the ALS population substructure and confirms that the Chiò classification system successfully identifies ALS subtypes. Additional validation is required to determine the accuracy and clinical use of these algorithms in assigning clinical subtypes. Nevertheless, our algorithms offer a broad insight into the clinical heterogeneity of ALS and help to determine the actual subtypes of disease that exist within this fatal neurodegenerative syndrome. The systematic identification of ALS subtypes will improve clinical care and clinical trial design. FUNDING US National Institute on Aging, US National Institutes of Health, Italian Ministry of Health, European Commission, University of Torino Rita Levi Montalcini Department of Neurosciences, Emilia Romagna Regional Health Authority, and Italian Ministry of Education, University, and Research. TRANSLATIONS For the Italian and German translations of the abstract see Supplementary Materials section.
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Affiliation(s)
- Faraz Faghri
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, US National Institute on Aging, Bethesda, MD, USA; Center for Alzheimer's and Related Dementias, US National Institute on Aging, Bethesda, MD, USA; Data Tecnica International, Glen Echo, MD, USA; Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Fabian Brunn
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Anant Dadu
- Center for Alzheimer's and Related Dementias, US National Institute on Aging, Bethesda, MD, USA; Data Tecnica International, Glen Echo, MD, USA; Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Elisabetta Zucchi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Ilaria Martinelli
- Neurology Unit, Department of Neurosciences, Azienda Ospedaliero Universitaria di Modena, Modena, Italy
| | - Letizia Mazzini
- ALS Centre, Department of Neurology, Maggiore della Carità University Hospital, Novara, Italy
| | - Rosario Vasta
- Rita Levi Montalcini, Department of Neuroscience, University of Turin, Turin, Italy
| | - Antonio Canosa
- Rita Levi Montalcini, Department of Neuroscience, University of Turin, Turin, Italy
| | - Cristina Moglia
- Rita Levi Montalcini, Department of Neuroscience, University of Turin, Turin, Italy
| | - Andrea Calvo
- Rita Levi Montalcini, Department of Neuroscience, University of Turin, Turin, Italy
| | - Michael A Nalls
- Center for Alzheimer's and Related Dementias, US National Institute on Aging, Bethesda, MD, USA; Data Tecnica International, Glen Echo, MD, USA
| | - Roy H Campbell
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Jessica Mandrioli
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy; Neurology Unit, Department of Neurosciences, Azienda Ospedaliero Universitaria di Modena, Modena, Italy
| | - Bryan J Traynor
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, US National Institute on Aging, Bethesda, MD, USA; Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA; Reta Lila Weston Institute, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Adriano Chiò
- Rita Levi Montalcini, Department of Neuroscience, University of Turin, Turin, Italy; Institute of Cognitive Sciences and Technologies, CNR, Rome, Italy; Neurology 1 and ALS Centre, Azienda Ospedaliero Universitaria Città della Salute e della Scienza, Turin, Italy
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12
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Stott S, Broza YY, Gharra A, Wang Z, Barker RA, Haick H. The Utility of Breath Analysis in the Diagnosis and Staging of Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2022; 12:993-1002. [PMID: 35147553 DOI: 10.3233/jpd-213133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
BACKGROUND The analysis of volatile organic compounds (VOCs) collected in breath samples has the potential to be a rapid, non-invasive test to aid in the clinical diagnosis and tracking of chronic conditions such as Parkinson's disease (PD). OBJECTIVE To assess the feasibility and utility of breath sample analysis done, both at point of collection in clinic and when sent away to be analyzed remotely, to diagnose, stratify and monitor disease course in a moderately large cohort of patients with PD. METHODS Breath samples were collected from 177 people with PD and 37 healthy matched control individuals followed over time. Standard clinical data (MDS-UPDRS & cognitive assessments) from the PD patients were collected at the same time as the breath sample was taken, these measures were then correlated with the breath test analysis of exhaled VOCs. RESULTS The breath test was able to distinguish patients with PD from healthy control participants and correlated with disease stage. The off-line system (remote analysis) gave good results with overall classification accuracies across a range of clinical measures of between 73.6% to 95.6%. The on-line (in clinic) system showed comparable results but with lower levels of correlation, varying between 33.5% to 82.4%. Chemical analysis identified 29 potential molecules that were different and which may relate to pathogenic pathways in PD. CONCLUSION Breath analysis shows potential for PD diagnostics and monitoring. Both off-line and on-line sensor systems were easy to do and provided comparable results which will enable this technique to be easily adopted in clinic if larger studies confirm our findings.
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Affiliation(s)
- Simon Stott
- John van Geest Centre for Brain Repair, Department of Clinical Neuroscience, University of Cambridge, Forvie Site, Cambridge, UK
| | - Yoav Y Broza
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, Israel
| | - Alaa Gharra
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, Israel
| | - Zhen Wang
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, Israel
| | - Roger A Barker
- John van Geest Centre for Brain Repair, Department of Clinical Neuroscience, University of Cambridge, Forvie Site, Cambridge, UK.,Wellcome-Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Hossam Haick
- Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, Israel
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13
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Makarious MB, Leonard HL, Vitale D, Iwaki H, Sargent L, Dadu A, Violich I, Hutchins E, Saffo D, Bandres-Ciga S, Kim JJ, Song Y, Maleknia M, Bookman M, Nojopranoto W, Campbell RH, Hashemi SH, Botia JA, Carter JF, Craig DW, Van Keuren-Jensen K, Morris HR, Hardy JA, Blauwendraat C, Singleton AB, Faghri F, Nalls MA. Multi-modality machine learning predicting Parkinson's disease. NPJ Parkinsons Dis 2022; 8:35. [PMID: 35365675 PMCID: PMC8975993 DOI: 10.1038/s41531-022-00288-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 02/01/2022] [Indexed: 02/06/2023] Open
Abstract
Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.
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Affiliation(s)
- Mary B Makarious
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
| | - Hampton L Leonard
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Glen Echo, MD, USA
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Dan Vitale
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Glen Echo, MD, USA
| | - Hirotaka Iwaki
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Glen Echo, MD, USA
| | - Lana Sargent
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
- School of Nursing, Virginia Commonwealth University, Richmond, VA, USA
- Geriatric Pharmacotherapy Program, School of Pharmacy, Virginia Commonwealth University, Richmond, VA, USA
| | - Anant Dadu
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Ivo Violich
- Institute of Translational Genomics, University of Southern California, Los Angeles, CA, USA
| | - Elizabeth Hutchins
- Neurogenomics Division, Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - David Saffo
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Sara Bandres-Ciga
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Jonggeol Jeff Kim
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
| | - Yeajin Song
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Glen Echo, MD, USA
| | | | - Matt Bookman
- Verily Life Sciences, South San Francisco, CA, USA
| | | | - Roy H Campbell
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Sayed Hadi Hashemi
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Juan A Botia
- Department of Molecular Neuroscience, UCL Queen Square Institute of Neurology, London, UK
- Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
| | | | - David W Craig
- Institute of Translational Genomics, University of Southern California, Los Angeles, CA, USA
| | | | - Huw R Morris
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
| | - John A Hardy
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
- UK Dementia Research Institute and Department of Neurodegenerative Disease and Reta Lila Weston Institute, London, UK
- Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong SAR, China
| | - Cornelis Blauwendraat
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Andrew B Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Faraz Faghri
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA.
- Data Tecnica International LLC, Glen Echo, MD, USA.
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA.
- Data Tecnica International LLC, Glen Echo, MD, USA.
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14
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LRRK2 and idiopathic Parkinson's disease. Trends Neurosci 2022; 45:224-236. [PMID: 34991886 PMCID: PMC8854345 DOI: 10.1016/j.tins.2021.12.002] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/30/2021] [Accepted: 12/09/2021] [Indexed: 12/22/2022]
Abstract
The etiology of idiopathic Parkinson's disease (iPD) is multifactorial, and both genetics and environmental exposures are risk factors. While mutations in leucine-rich repeat kinase-2 (LRRK2) that are associated with increased kinase activity are the most common cause of autosomal dominant PD, the role of LRRK2 in iPD, independent of mutations, remains uncertain. In this review, we discuss how the architecture of LRRK2 influences kinase activation and how enhanced LRRK2 substrate phosphorylation might contribute to pathogenesis. We describe how oxidative stress and endolysosomal dysfunction, both of which occur in iPD, can activate non-mutated LRRK2 to a similar degree as pathogenic mutations. Similarly, environmental toxicants that are linked epidemiologically to iPD risk can also activate LRRK2. In aggregate, current evidence suggests an important role for LRRK2 in iPD.
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15
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Pihlstrøm L, Fan CC, Frei O, Blauwendraat C, Bandres-Ciga S, Dale AM, Seibert TM, Andreassen OA, Dale AM, Seibert TM, Andreassen OA. Genetic Stratification of Age-Dependent Parkinson's Disease Risk by Polygenic Hazard Score. Mov Disord 2022; 37:62-69. [PMID: 34612543 PMCID: PMC9843635 DOI: 10.1002/mds.28808] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 09/08/2021] [Accepted: 09/13/2021] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Parkinson's disease (PD) is a highly age-related disorder, where common genetic risk variants affect both disease risk and age at onset. A statistical approach that integrates these effects across all common variants may be clinically useful for individual risk stratification. A polygenic hazard score methodology, leveraging a time-to-event framework, has recently been successfully applied in other age-related disorders. OBJECTIVES We aimed to develop and validate a polygenic hazard score model in sporadic PD. METHODS Using a Cox regression framework, we modeled the polygenic hazard score in a training data set of 11,693 PD patients and 9841 controls. The score was then validated in an independent test data set of 5112 PD patients and 5372 controls and a small single-study sample of 360 patients and 160 controls. RESULTS A polygenic hazard score predicts the onset of PD with a hazard ratio of 3.78 (95% confidence interval 3.49-4.10) when comparing the highest to the lowest risk decile. Combined with epidemiological data on incidence rate, we apply the score to estimate genetically stratified instantaneous PD risk across age groups. CONCLUSIONS We demonstrate the feasibility of a polygenic hazard approach in PD, integrating the genetic effects on disease risk and age at onset in a single model. In combination with other predictive biomarkers, the approach may hold promise for risk stratification in future clinical trials of disease-modifying therapies, which aim at postponing the onset of PD. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Lasse Pihlstrøm
- Department of Neurology, Oslo University Hospital, Oslo, Norway,Corresponding authors at: Department of Neurology, Oslo University Hospital, PO Box 4950 Nydalen, 0424 Oslo, Norway. , NORMENT Centre, Oslo University Hospital, Ullevål, PO Box 4956 Nydalen, 0424 Oslo, Norway.
| | - Chun Chieh Fan
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA,Center for Multimodal Imaging and Genetics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Oleksandr Frei
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Center for Bioinformatics, Department of Informatics, University of Oslo
| | - Cornelis Blauwendraat
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MY, USA
| | - Sara Bandres-Ciga
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MY, USA
| | | | - Anders M. Dale
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA,Department of Radiology, University of California San Diego, La Jolla, CA, USA,Department of Neurosciences, University of California, San Diego, La Jolla, California, United States of America
| | - Tyler M. Seibert
- Department of Radiology, University of California San Diego, La Jolla, CA, USA,Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, USA,Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Anders M Dale
- Department of Cognitive Science, University of California San Diego, La Jolla, California, USA.,Center for Multimodal Imaging and Genetics, School of Medicine, University of California San Diego, La Jolla, California, USA.,NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Radiology, University of California San Diego, La Jolla, California, USA.,Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Tyler M Seibert
- Center for Multimodal Imaging and Genetics, School of Medicine, University of California San Diego, La Jolla, California, USA.,NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA.,Department of Radiology, University of California San Diego, La Jolla, California, USA.,Department of Bioengineering, University of California San Diego, La Jolla, California, USA
| | - Ole A Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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16
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Cross B, Turner R, Pirmohamed M. Polygenic risk scores: An overview from bench to bedside for personalised medicine. Front Genet 2022; 13:1000667. [PMID: 36437929 PMCID: PMC9692112 DOI: 10.3389/fgene.2022.1000667] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 10/24/2022] [Indexed: 11/13/2022] Open
Abstract
Since the first polygenic risk score (PRS) in 2007, research in this area has progressed significantly. The increasing number of SNPs that have been identified by large scale GWAS analyses has fuelled the development of a myriad of PRSs for a wide variety of diseases and, more recently, to PRSs that potentially identify differential response to specific drugs. PRSs constitute a composite genomic biomarker and potential applications for PRSs in clinical practice encompass risk prediction and disease screening, early diagnosis, prognostication, and drug stratification to improve efficacy or reduce adverse drug reactions. Nevertheless, to our knowledge, no PRSs have yet been adopted into routine clinical practice. Beyond the technical considerations of PRS development, the major challenges that face PRSs include demonstrating clinical utility and circumnavigating the implementation of novel genomic technologies at scale into stretched healthcare systems. In this review, we discuss progress in developing disease susceptibility PRSs across multiple medical specialties, development of pharmacogenomic PRSs, and future directions for the field.
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Affiliation(s)
- Benjamin Cross
- The Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Richard Turner
- The Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Munir Pirmohamed
- The Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, United Kingdom
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17
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Senkevich K, Rudakou U, Gan-Or Z. New therapeutic approaches to Parkinson's disease targeting GBA, LRRK2 and Parkin. Neuropharmacology 2021; 202:108822. [PMID: 34626666 DOI: 10.1016/j.neuropharm.2021.108822] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 10/01/2021] [Accepted: 10/04/2021] [Indexed: 01/23/2023]
Abstract
Parkinson's disease (PD) is defined as a complex disorder with multifactorial pathogenesis, yet a more accurate definition could be that PD is not a single entity, but rather a mixture of different diseases with similar phenotypes. Attempts to classify subtypes of PD have been made based on clinical phenotypes or biomarkers. However, the most practical approach, at least for a portion of the patients, could be to classify patients based on genes involved in PD. GBA and LRRK2 mutations are the most common genetic causes or risk factors of PD, and PRKN is the most common cause of autosomal recessive form of PD. Patients carrying variants in GBA, LRRK2 or PRKN differ in some of their clinical characteristics, pathology and biochemical parameters. Thus, these three PD-associated genes are of special interest for drug development. Existing therapeutic approaches in PD are strictly symptomatic, as numerous clinical trials aimed at modifying PD progression or providing neuroprotection have failed over the last few decades. The lack of precision medicine approach in most of these trials could be one of the reasons why they were not successful. In the current review we discuss novel therapeutic approaches targeting GBA, LRRK2 and PRKN and discuss different aspects related to these genes and clinical trials.
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Affiliation(s)
- Konstantin Senkevich
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montréal, QC, Canada; Department of Neurology and neurosurgery, McGill University, Montréal, QC, Canada; First Pavlov State Medical University of St. Petersburg, Saint-Petersburg, Russia
| | - Uladzislau Rudakou
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montréal, QC, Canada; Department of Neurology and neurosurgery, McGill University, Montréal, QC, Canada; Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Ziv Gan-Or
- The Neuro (Montreal Neurological Institute-Hospital), McGill University, Montréal, QC, Canada; Department of Neurology and neurosurgery, McGill University, Montréal, QC, Canada; Department of Human Genetics, McGill University, Montréal, QC, Canada.
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18
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Iwaki H, Leonard HL, Makarious MB, Bookman M, Landin B, Vismer D, Casey B, Gibbs JR, Hernandez DG, Blauwendraat C, Vitale D, Song Y, Kumar D, Dalgard CL, Sadeghi M, Dong X, Misquitta L, Scholz SW, Scherzer CR, Nalls MA, Biswas S, Singleton AB. Accelerating Medicines Partnership: Parkinson's Disease. Genetic Resource. Mov Disord 2021; 36:1795-1804. [PMID: 33960523 PMCID: PMC8453903 DOI: 10.1002/mds.28549] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 01/20/2021] [Accepted: 02/11/2021] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Whole-genome sequencing data are available from several large studies across a variety of diseases and traits. However, massive storage and computation resources are required to use these data, and to achieve sufficient power for discoveries, harmonization of multiple cohorts is critical. OBJECTIVES The Accelerating Medicines Partnership Parkinson's Disease program has developed a research platform for Parkinson's disease (PD) that integrates the storage and analysis of whole-genome sequencing data, RNA expression data, and clinical data, harmonized across multiple cohort studies. METHODS The version 1 release contains whole-genome sequencing data derived from 3941 participants from 4 cohorts. Samples underwent joint genotyping by the TOPMed Freeze 9 Variant Calling Pipeline. We performed descriptive analyses of these whole-genome sequencing data using the Accelerating Medicines Partnership Parkinson's Disease platform. RESULTS The clinical diagnosis of participants in version 1 release includes 2005 idiopathic PD patients, 963 healthy controls, 64 prodromal subjects, 62 clinically diagnosed PD subjects without evidence of dopamine deficit, and 705 participants of genetically enriched cohorts carrying PD risk-associated GBA variants or LRRK2 variants, of whom 304 were affected. We did not observe significant enrichment of pathogenic variants in the idiopathic PD group, but the polygenic risk score was higher in PD both in nongenetically enriched cohorts and genetically enriched cohorts. The population analysis showed a correlation between genetically enriched cohorts and Ashkenazi Jewish ancestry. CONCLUSIONS We describe the genetic component of the Accelerating Medicines Partnership Parkinson's Disease platform, a solution to democratize data access and analysis for the PD research community. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article is a U.S. Government work and is in the public domain in the USA.
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Affiliation(s)
- Hirotaka Iwaki
- Data Tecnica InternationalGlen EchoMarylandUSA
- Center for Alzheimer's and Related DementiasNational Institute on AgingBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | - Hampton L. Leonard
- Data Tecnica InternationalGlen EchoMarylandUSA
- Center for Alzheimer's and Related DementiasNational Institute on AgingBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | - Mary B. Makarious
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | | | | | | | - Bradford Casey
- The Michael J. Fox Foundation for Parkinson's ResearchNew YorkNew YorkUSA
| | - J. Raphael Gibbs
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | - Dena G. Hernandez
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | | | - Daniel Vitale
- Data Tecnica InternationalGlen EchoMarylandUSA
- Center for Alzheimer's and Related DementiasNational Institute on AgingBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | - Yeajin Song
- Data Tecnica InternationalGlen EchoMarylandUSA
- Center for Alzheimer's and Related DementiasNational Institute on AgingBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | | | - Clifton L. Dalgard
- Department of Anatomy, Physiology & GeneticsUniformed Services University of the Health SciencesBethesdaMarylandUSA
- The American Genome CenterUniformed Services University of the Health SciencesBethesdaMarylandUSA
| | - Mahdiar Sadeghi
- SanofiFraminghamMassachusettsUSA
- Northeastern UniversityBostonMassachusettsUSA
| | - Xianjun Dong
- Harvard Medical SchoolBrigham and Women's HospitalBostonMassachusettsUSA
| | | | - Sonja W. Scholz
- National Institute of Neurological Disorders and StrokeBethesdaMarylandUSA
- Department of NeurologyJohns Hopkins UniversityBaltimoreMarylandUSA
| | | | - Mike A. Nalls
- Data Tecnica InternationalGlen EchoMarylandUSA
- Center for Alzheimer's and Related DementiasNational Institute on AgingBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | | | - Andrew B. Singleton
- Center for Alzheimer's and Related DementiasNational Institute on AgingBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
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19
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Day JO, Mullin S. The Genetics of Parkinson's Disease and Implications for Clinical Practice. Genes (Basel) 2021; 12:genes12071006. [PMID: 34208795 PMCID: PMC8304082 DOI: 10.3390/genes12071006] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/21/2021] [Accepted: 06/28/2021] [Indexed: 12/17/2022] Open
Abstract
The genetic landscape of Parkinson’s disease (PD) is characterised by rare high penetrance pathogenic variants causing familial disease, genetic risk factor variants driving PD risk in a significant minority in PD cases and high frequency, low penetrance variants, which contribute a small increase of the risk of developing sporadic PD. This knowledge has the potential to have a major impact in the clinical care of people with PD. We summarise these genetic influences and discuss the implications for therapeutics and clinical trial design.
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Affiliation(s)
- Jacob Oliver Day
- Faculty of Health, University of Plymouth, Plymouth PL4 8AA, UK;
| | - Stephen Mullin
- Faculty of Health, University of Plymouth, Plymouth PL4 8AA, UK;
- Department of Clinical and Movement Neurosciences, University College London Institute of Neurology, London WC1N 3BG, UK
- Correspondence:
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20
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Advancing Personalized Medicine in Common Forms of Parkinson's Disease through Genetics: Current Therapeutics and the Future of Individualized Management. J Pers Med 2021; 11:jpm11030169. [PMID: 33804504 PMCID: PMC7998972 DOI: 10.3390/jpm11030169] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 02/16/2021] [Accepted: 02/19/2021] [Indexed: 02/07/2023] Open
Abstract
Parkinson’s disease (PD) is a condition with heterogeneous clinical manifestations that vary in age at onset, rate of progression, disease course, severity, motor and non-motor symptoms, and a variable response to antiparkinsonian drugs. It is considered that there are multiple PD etiological subtypes, some of which could be predicted by genetics. The characterization and prediction of these distinct molecular entities provides a growing opportunity to use individualized management and personalized therapies. Dissecting the genetic architecture of PD is a critical step in identifying therapeutic targets, and genetics represents a step forward to sub-categorize and predict PD risk and progression. A better understanding and separation of genetic subtypes has immediate implications in clinical trial design by unraveling the different flavors of clinical presentation and development. Personalized medicine is a nascent area of research and represents a paramount challenge in the treatment and cure of PD. This manuscript summarizes the current state of precision medicine in the PD field and discusses how genetics has become the engine to gain insights into disease during our constant effort to develop potential etiological based interventions.
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21
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von Linstow CU, Gan-Or Z, Brundin P. Precision medicine in Parkinson's disease patients with LRRK2 and GBA risk variants - Let's get even more personal. Transl Neurodegener 2020; 9:39. [PMID: 33066808 PMCID: PMC7565766 DOI: 10.1186/s40035-020-00218-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 09/22/2020] [Indexed: 12/15/2022] Open
Abstract
Parkinson's disease (PD) is characterized by motor deficits and a wide variety of non-motor symptoms. The age of onset, rate of disease progression and the precise profile of motor and non-motor symptoms display considerable individual variation. Neuropathologically, the loss of substantia nigra dopaminergic neurons is a key feature of PD. The vast majority of PD patients exhibit alpha-synuclein aggregates in several brain regions, but there is also great variability in the neuropathology between individuals. While the dopamine replacement therapies can reduce motor symptoms, current therapies do not modify the disease progression. Numerous clinical trials using a wide variety of approaches have failed to achieve disease modification. It has been suggested that the heterogeneity of PD is a major contributing factor to the failure of disease modification trials, and that it is unlikely that a single treatment will be effective in all patients. Precision medicine, using drugs designed to target the pathophysiology in a manner that is specific to each individual with PD, has been suggested as a way forward. PD patients can be stratified according to whether they carry one of the risk variants associated with elevated PD risk. In this review we assess current clinical trials targeting two enzymes, leucine-rich repeat kinase 2 (LRRK2) and glucocerebrosidase (GBA), which are encoded by two most common PD risk genes. Because the details of the pathogenic processes coupled to the different LRRK2 and GBA risk variants are not fully understood, we ask if these precision medicine-based intervention strategies will prove "precise" or "personalized" enough to modify the disease process in PD patients. We also consider at what phases of the disease that such strategies might be effective, in light of the genes being primarily associated with the risk of developing disease in the first place, and less clearly linked to the rate of disease progression. Finally, we critically evaluate the notion that therapies targeting LRRK2 and GBA might be relevant to a wider segment of PD patients, beyond those that actually carry risk variants of these genes.
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Affiliation(s)
| | - Ziv Gan-Or
- Montreal Neurological Institute, McGill University, Montréal, QC, H3A 2B4, Canada.,Department of Human Genetics, McGill University, Montréal, QC, H3A 0C7, Canada.,Department of Neurology and Neurosurgery, McGill University, Montréal, QC, H3A 2B4, Canada
| | - Patrik Brundin
- Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, 49503, USA
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22
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Abstract
In recent years, a precision medicine approach, which customizes medical treatments based on patients' individual profiles and incorporates variability in genes, the environment, and lifestyle, has transformed medical care in numerous medical fields, most notably oncology. Applying a similar approach to Parkinson's disease (PD) may promote the development of disease-modifying agents that could help slow progression or possibly even avert disease development in a subset of at-risk individuals. The urgent need for such trials partially stems from the negative results of clinical trials where interventions treat all PD patients as a single homogenous group. Here, we review the current obstacles towards the development of precision interventions in PD. We also review and discuss the clinical trials that target genetic forms of PD, i.e., GBA-associated and LRRK2-associated PD.
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Affiliation(s)
- Susanne A Schneider
- Department of Neurology, Ludwig-Maximilians-University of München, Marchioninistr. 15, 81377, Munich, Germany.
| | - Baccara Hizli
- Department of Neurology, Ludwig-Maximilians-University of München, Marchioninistr. 15, 81377, Munich, Germany
| | - Roy N Alcalay
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA.
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23
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Jacobs BM, Belete D, Bestwick J, Blauwendraat C, Bandres-Ciga S, Heilbron K, Dobson R, Nalls MA, Singleton A, Hardy J, Giovannoni G, Lees AJ, Schrag AE, Noyce AJ. Parkinson's disease determinants, prediction and gene-environment interactions in the UK Biobank. J Neurol Neurosurg Psychiatry 2020; 91:1046-1054. [PMID: 32934108 PMCID: PMC7509524 DOI: 10.1136/jnnp-2020-323646] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 06/30/2020] [Accepted: 07/02/2020] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To systematically investigate the association of environmental risk factors and prodromal features with incident Parkinson's disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate whether existing risk prediction algorithms are improved by the inclusion of genetic risk scores. METHODS We identified individuals with an incident diagnosis of PD (n=1276) and controls (n=500 406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. We constructed polygenic risk scores (PRSs) using external weights and selected the best PRS from a subset of the cohort (30%). The PRS was used in a separate testing set (70%) to examine gene-environment interactions and compare predictive models for PD. RESULTS Strong evidence of association (false discovery rate <0.05) was found between PD and a positive family history of PD, a positive family history of dementia, non-smoking, low alcohol consumption, depression, daytime somnolence, epilepsy and earlier menarche. Individuals with the highest 10% of PRSs had increased risk of PD (OR 3.37, 95% CI 2.41 to 4.70) compared with the lowest risk decile. A higher PRS was associated with earlier age at PD diagnosis and inclusion of the PRS in the PREDICT-PD algorithm led to a modest improvement in model performance. We found evidence of an interaction between the PRS and diabetes. INTERPRETATION Here, we used UK Biobank data to reproduce several well-known associations with PD, to demonstrate the validity of a PRS and to demonstrate a novel gene-environment interaction, whereby the effect of diabetes on PD risk appears to depend on background genetic risk for PD.
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Affiliation(s)
- Benjamin Meir Jacobs
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, London, UK
| | - Daniel Belete
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, London, UK
| | - Jonathan Bestwick
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, London, UK
| | - Cornelis Blauwendraat
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Sara Bandres-Ciga
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Ruth Dobson
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, London, UK
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Andrew Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - John Hardy
- Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK
| | - Gavin Giovannoni
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, London, UK.,Centre for Neuroscience and Trauma, Barts and The London School of Medicine and Dentistry, Blizard Institute, London, UK
| | - Andrew John Lees
- Reta Lila Weston Institute of Neurological Studies and Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, London, UK
| | - Anette-Eleonore Schrag
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, London, UK
| | - Alastair J Noyce
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, London, UK .,Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, London, UK
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24
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Clarimón J. Genetic-environmental factors finally assessed together in Parkinson's disease. J Neurol Neurosurg Psychiatry 2020; 91:1030. [PMID: 32934106 DOI: 10.1136/jnnp-2020-324472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 07/15/2020] [Indexed: 11/03/2022]
Affiliation(s)
- Jordi Clarimón
- Genetics of Neurodegenerative Disorders Unit, Sant Pau Biomedical Research Institute,Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
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25
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Su C, Tong J, Wang F. Mining genetic and transcriptomic data using machine learning approaches in Parkinson's disease. NPJ PARKINSONS DISEASE 2020; 6:24. [PMID: 32964109 PMCID: PMC7481248 DOI: 10.1038/s41531-020-00127-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 08/13/2020] [Indexed: 01/08/2023]
Abstract
High-throughput techniques have generated abundant genetic and transcriptomic data of Parkinson’s disease (PD) patients but data analysis approaches such as traditional statistical methods have not provided much in the way of insightful integrated analysis or interpretation of the data. As an advanced computational approach, machine learning, which enables people to identify complex patterns and insight from data, has consequently been harnessed to analyze and interpret large, highly complex genetic and transcriptomic data toward a better understanding of PD. In particular, machine learning models have been developed to integrate patient genotype data alone or combined with demographic, clinical, neuroimaging, and other information, for PD outcome study. They have also been used to identify biomarkers of PD based on transcriptomic data, e.g., gene expression profiles from microarrays. This study overviews the relevant literature on using machine learning models for genetic and transcriptomic data analysis in PD, points out remaining challenges, and suggests future directions accordingly. Undoubtedly, the use of machine learning is amplifying PD genetic and transcriptomic achievements for accelerating the study of PD. Existing studies have demonstrated the great potential of machine learning in discovering hidden patterns within genetic or transcriptomic information and thus revealing clues underpinning pathology and pathogenesis. Moving forward, by addressing the remaining challenges, machine learning may advance our ability to precisely diagnose, prognose, and treat PD.
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Affiliation(s)
- Chang Su
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY USA
| | - Jie Tong
- Department of Mechanical and Aerospace Engineering, New York University, New York, NY USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY USA
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26
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Blauwendraat C, Reed X, Krohn L, Heilbron K, Bandres-Ciga S, Tan M, Gibbs JR, Hernandez DG, Kumaran R, Langston R, Bonet-Ponce L, Alcalay RN, Hassin-Baer S, Greenbaum L, Iwaki H, Leonard HL, Grenn FP, Ruskey JA, Sabir M, Ahmed S, Makarious MB, Pihlstrøm L, Toft M, van Hilten JJ, Marinus J, Schulte C, Brockmann K, Sharma M, Siitonen A, Majamaa K, Eerola-Rautio J, Tienari PJ, Pantelyat A, Hillis AE, Dawson TM, Rosenthal LS, Albert MS, Resnick SM, Ferrucci L, Morris CM, Pletnikova O, Troncoso J, Grosset D, Lesage S, Corvol JC, Brice A, Noyce AJ, Masliah E, Wood N, Hardy J, Shulman LM, Jankovic J, Shulman JM, Heutink P, Gasser T, Cannon P, Scholz SW, Morris H, Cookson MR, Nalls MA, Gan-Or Z, Singleton AB. Genetic modifiers of risk and age at onset in GBA associated Parkinson's disease and Lewy body dementia. Brain 2020; 143:234-248. [PMID: 31755958 DOI: 10.1093/brain/awz350] [Citation(s) in RCA: 133] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 09/07/2019] [Accepted: 09/17/2019] [Indexed: 11/14/2022] Open
Abstract
Parkinson's disease is a genetically complex disorder. Multiple genes have been shown to contribute to the risk of Parkinson's disease, and currently 90 independent risk variants have been identified by genome-wide association studies. Thus far, a number of genes (including SNCA, LRRK2, and GBA) have been shown to contain variability across a spectrum of frequency and effect, from rare, highly penetrant variants to common risk alleles with small effect sizes. Variants in GBA, encoding the enzyme glucocerebrosidase, are associated with Lewy body diseases such as Parkinson's disease and Lewy body dementia. These variants, which reduce or abolish enzymatic activity, confer a spectrum of disease risk, from 1.4- to >10-fold. An outstanding question in the field is what other genetic factors that influence GBA-associated risk for disease, and whether these overlap with known Parkinson's disease risk variants. Using multiple, large case-control datasets, totalling 217 165 individuals (22 757 Parkinson's disease cases, 13 431 Parkinson's disease proxy cases, 622 Lewy body dementia cases and 180 355 controls), we identified 1691 Parkinson's disease cases, 81 Lewy body dementia cases, 711 proxy cases and 7624 controls with a GBA variant (p.E326K, p.T369M or p.N370S). We performed a genome-wide association study and analysed the most recent Parkinson's disease-associated genetic risk score to detect genetic influences on GBA risk and age at onset. We attempted to replicate our findings in two independent datasets, including the personal genetics company 23andMe, Inc. and whole-genome sequencing data. Our analysis showed that the overall Parkinson's disease genetic risk score modifies risk for disease and decreases age at onset in carriers of GBA variants. Notably, this effect was consistent across all tested GBA risk variants. Dissecting this signal demonstrated that variants in close proximity to SNCA and CTSB (encoding cathepsin B) are the most significant contributors. Risk variants in the CTSB locus were identified to decrease mRNA expression of CTSB. Additional analyses suggest a possible genetic interaction between GBA and CTSB and GBA p.N370S induced pluripotent cell-derived neurons were shown to have decreased cathepsin B expression compared to controls. These data provide a genetic basis for modification of GBA-associated Parkinson's disease risk and age at onset, although the total contribution of common genetics variants is not large. We further demonstrate that common variability at genes implicated in lysosomal function exerts the largest effect on GBA associated risk for disease. Further, these results have implications for selection of GBA carriers for therapeutic interventions.
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Affiliation(s)
- Cornelis Blauwendraat
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Xylena Reed
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Lynne Krohn
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | | | - Sara Bandres-Ciga
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Manuela Tan
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
| | - J Raphael Gibbs
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Dena G Hernandez
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Ravindran Kumaran
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Rebekah Langston
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Luis Bonet-Ponce
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Roy N Alcalay
- Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA.,Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA
| | - Sharon Hassin-Baer
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Department of Neurology, Sheba Medical Center, Tel Hashomer, Israel.,Movement Disorders Institute, Sheba Medical Center, Tel Hashomer, Israel.,The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel Hashomer, Israel
| | - Lior Greenbaum
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel Hashomer, Israel.,The Danek Gertner Institute of Human Genetics, Sheba Medical Center, Tel Hashomer, Israel
| | - Hirotaka Iwaki
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Hampton L Leonard
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Francis P Grenn
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer A Ruskey
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Marya Sabir
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Sarah Ahmed
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Mary B Makarious
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Lasse Pihlstrøm
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Mathias Toft
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Jacobus J van Hilten
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Johan Marinus
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Claudia Schulte
- Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany
| | - Kathrin Brockmann
- Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany
| | - Manu Sharma
- Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tubingen, Germany
| | - Ari Siitonen
- Institute of Clinical Medicine, Department of Neurology, University of Oulu, Oulu, Finland.,Department of Neurology and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Kari Majamaa
- Institute of Clinical Medicine, Department of Neurology, University of Oulu, Oulu, Finland.,Department of Neurology and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Johanna Eerola-Rautio
- Department of Neurology, Helsinki University Hospital, and Molecular Neurology, Research Programs Unit, Biomedicum, University of Helsinki, Helsinki, Finland
| | - Pentti J Tienari
- Department of Neurology, Helsinki University Hospital, and Molecular Neurology, Research Programs Unit, Biomedicum, University of Helsinki, Helsinki, Finland
| | | | - Alexander Pantelyat
- Neuroregeneration and Stem Cell Program, Institute for Cell Engineering, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Argye E Hillis
- Neuroregeneration and Stem Cell Program, Institute for Cell Engineering, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Ted M Dawson
- Neuroregeneration and Stem Cell Program, Institute for Cell Engineering, Johns Hopkins University Medical Center, Baltimore, MD, USA.,Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Liana S Rosenthal
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD, USA
| | - Christopher M Morris
- Newcastle Brain Tissue Resource, Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK
| | - Olga Pletnikova
- Department of Pathology (Neuropathology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Juan Troncoso
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA.,Department of Pathology (Neuropathology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Donald Grosset
- Department of Neurology, Institute of Neurological Sciences, Queen Elizabeth University Hospital, Glasgow, UK
| | - Suzanne Lesage
- Inserm U1127, Sorbonne Universités, UPMC Univ Paris 06 UMR S1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Jean-Christophe Corvol
- Inserm U1127, Sorbonne Universités, UPMC Univ Paris 06 UMR S1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Alexis Brice
- Inserm U1127, Sorbonne Universités, UPMC Univ Paris 06 UMR S1127, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - Alastair J Noyce
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK.,Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
| | - Eliezer Masliah
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Nick Wood
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
| | - John Hardy
- Department of Neurodegenerative Diseases, UCL Queen Square Institute of Neurology, London, UK
| | - Lisa M Shulman
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Joseph Jankovic
- Department of Neurology, Baylor College of Medicine, Houston, USA
| | - Joshua M Shulman
- Department of Neurology, Baylor College of Medicine, Houston, USA.,Departments of Molecular and Human Genetics and Neuroscience, Baylor College of Medicine, Houston, USA.,Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, USA
| | - Peter Heutink
- Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany
| | - Thomas Gasser
- Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.,German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany
| | | | - Sonja W Scholz
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.,Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Huw Morris
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
| | - Mark R Cookson
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.,Data Tecnica International, Glen Echo, MD, USA
| | - Ziv Gan-Or
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.,Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Andrew B Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
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27
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Videnovic A, Ju YES, Arnulf I, Cochen-De Cock V, Högl B, Kunz D, Provini F, Ratti PL, Schiess MC, Schenck CH, Trenkwalder C. Clinical trials in REM sleep behavioural disorder: challenges and opportunities. J Neurol Neurosurg Psychiatry 2020; 91:740-749. [PMID: 32404379 PMCID: PMC7735522 DOI: 10.1136/jnnp-2020-322875] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 03/31/2020] [Accepted: 04/17/2020] [Indexed: 01/13/2023]
Abstract
The rapid eye movement sleep behavioural disorder (RBD) population is an ideal study population for testing disease-modifying treatments for synucleinopathies, since RBD represents an early prodromal stage of synucleinopathy when neuropathology may be more responsive to treatment. While clonazepam and melatonin are most commonly used as symptomatic treatments for RBD, clinical trials of symptomatic treatments are also needed to identify evidence-based treatments. A comprehensive framework for both disease-modifying and symptomatic treatment trials in RBD is described, including potential treatments in the pipeline, cost-effective participant recruitment and selection, study design, outcomes and dissemination of results. For disease-modifying treatment clinical trials, the recommended primary outcome is phenoconversion to an overt synucleinopathy, and stratification features should be used to select a study population at high risk of phenoconversion, to enable more rapid clinical trials. For symptomatic treatment clinical trials, objective polysomnogram-based measurement of RBD-related movements and vocalisations should be the primary outcome measure, rather than subjective scales or diaries. Mobile technology to enable objective measurement of RBD episodes in the ambulatory setting, and advances in imaging, biofluid, tissue, and neurophysiological biomarkers of synucleinopathies, will enable more efficient clinical trials but are still in development. Increasing awareness of RBD among the general public and medical community coupled with timely diagnosis of these diseases will facilitate progress in the development of therapeutics for RBD and associated neurodegenerative disorders.
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Affiliation(s)
- Aleksandar Videnovic
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Yo-El S Ju
- Department of Neurology, Washington University in Saint Louis, Saint Louis, Missouri, USA
| | - Isabelle Arnulf
- Assistance Publique Hôpitaux de Paris, Service des pathologies du Sommeil, Hôpital Pitié-Salpêtrière, Paris, France.,UMR S 1127, CNRS UMR 7225, ICM, Sorbonne Universités, UPMC University Paris, Paris, France
| | - Valérie Cochen-De Cock
- Neurologie et sommeil, Clinique Beau Soleil, Montpellier, France.,Laboratoire Movement to Health (M2H), EuroMov, Université Montpellier, Montpellier, France
| | - Birgit Högl
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Dieter Kunz
- Clinic for Sleep and Chronomedicine, Berlin, Germany
| | - Federica Provini
- IRCCS Institute of Neurological Sciences of Bologna, University of Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | | | - Mya C Schiess
- Department of Neurology, University of Texas Medical School at Houston, Houston, Texas, USA
| | - Carlos H Schenck
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota, USA.,Minnesota Regional Sleep Disorders Center, Minneapolis, Minnesota, USA
| | - Claudia Trenkwalder
- Paracelsus Elena Klinik, Kassel, Germany.,Department of Neurosurgery, University Medical Center, Göttingen, Germany
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Bandres-Ciga S, Diez-Fairen M, Kim JJ, Singleton AB. Genetics of Parkinson's disease: An introspection of its journey towards precision medicine. Neurobiol Dis 2020; 137:104782. [PMID: 31991247 PMCID: PMC7064061 DOI: 10.1016/j.nbd.2020.104782] [Citation(s) in RCA: 204] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 01/15/2020] [Accepted: 01/24/2020] [Indexed: 12/15/2022] Open
Abstract
A substantial proportion of risk for Parkinson's disease (PD) is driven by genetics. Progress in understanding the genetic basis of PD has been significant. So far, highly-penetrant rare genetic alterations in SNCA, LRRK2, VPS35, PRKN, PINK1, DJ-1 and GBA have been linked with typical familial PD and common genetic variability at 90 loci have been linked to risk for PD. In this review, we outline the journey thus far of PD genetics, highlighting how significant advances have improved our knowledge of the genetic basis of PD risk, onset and progression. Despite remarkable progress, our field has yet to unravel how genetic risk variants disrupt biological pathways and molecular networks underlying the pathobiology of the disease. We highlight that currently identified genetic risk factors only represent a fraction of the likely genetic risk for PD. Identifying the remaining genetic risk will require us to diversify our efforts, performing genetic studies across different ancestral groups. This work will inform us on the varied genetic basis of disease across populations and also aid in fine mapping discovered loci. If we are able to take this course, we foresee that genetic discoveries in PD will directly influence our ability to predict disease and aid in defining etiological subtypes, critical steps for the implementation of precision medicine for PD.
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Affiliation(s)
- Sara Bandres-Ciga
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA; Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), Granada 18016, Spain.
| | - Monica Diez-Fairen
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA; Fundació Docència i Recerca Mútua Terrassa and Movement Disorders Unit, Department of Neurology, University Hospital Mútua Terrassa, Terrassa 08221, Barcelona, Spain
| | - Jonggeol Jeff Kim
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
| | - Andrew B Singleton
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA.
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