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Liampas I, Kyriakoulopoulou P, Siokas V, Tsiamaki E, Stamati P, Kefalopoulou Z, Chroni E, Dardiotis E. Apolipoprotein E Gene in α-Synucleinopathies: A Narrative Review. Int J Mol Sci 2024; 25:1795. [PMID: 38339074 PMCID: PMC10855384 DOI: 10.3390/ijms25031795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 01/27/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
In this narrative review, we delved into the intricate interplay between Apolipoprotein E (APOE) alleles (typically associated with Alzheimer's disease-AD) and alpha-synucleinopathies (aS-pathies), involving Parkinson's disease (PD), Parkinson's disease dementia (PDD), dementia with Lewy bodies (DLB), and multiple-system atrophy (MSA). First, in-vitro, animal, and human-based data on the exacerbating effect of APOE4 on LB pathology were summarized. We found robust evidence that APOE4 carriage constitutes a risk factor for PDD-APOE2, and APOE3 may not alter the risk of developing PDD. We confirmed that APOE4 copies confer an increased hazard towards DLB, as well. Again APOE2 and APOE3 appear unrelated to the risk of conversion. Of note, in individuals with DLB APOE4, carriage appears to be intermediately prevalent between AD and PDD-PD (AD > DLB > PDD > PD). Less consistency existed when it came to PD; APOE-PD associations tended to be markedly modified by ethnicity. Finally, we failed to establish an association between the APOE gene and MSA. Phenotypic associations (age of disease onset, survival, cognitive-neuropsychiatric- motor-, and sleep-related manifestations) between APOE alleles, and each of the aforementioned conditions were also outlined. Finally, a synopsis of literature gaps was provided followed by suggestions for future research.
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Affiliation(s)
- Ioannis Liampas
- Department of Neurology, University Hospital of Larissa, School of Medicine, University of Thessaly, 41100 Larissa, Greece; (V.S.); (P.S.); (E.D.)
| | - Panagiota Kyriakoulopoulou
- Department of Neurology, University Hospital of Patras, School of Medicine, University of Patras, 26504 Rio Patras, Greece; (P.K.); (E.T.); (Z.K.); (E.C.)
| | - Vasileios Siokas
- Department of Neurology, University Hospital of Larissa, School of Medicine, University of Thessaly, 41100 Larissa, Greece; (V.S.); (P.S.); (E.D.)
| | - Eirini Tsiamaki
- Department of Neurology, University Hospital of Patras, School of Medicine, University of Patras, 26504 Rio Patras, Greece; (P.K.); (E.T.); (Z.K.); (E.C.)
| | - Polyxeni Stamati
- Department of Neurology, University Hospital of Larissa, School of Medicine, University of Thessaly, 41100 Larissa, Greece; (V.S.); (P.S.); (E.D.)
| | - Zinovia Kefalopoulou
- Department of Neurology, University Hospital of Patras, School of Medicine, University of Patras, 26504 Rio Patras, Greece; (P.K.); (E.T.); (Z.K.); (E.C.)
| | - Elisabeth Chroni
- Department of Neurology, University Hospital of Patras, School of Medicine, University of Patras, 26504 Rio Patras, Greece; (P.K.); (E.T.); (Z.K.); (E.C.)
| | - Efthimios Dardiotis
- Department of Neurology, University Hospital of Larissa, School of Medicine, University of Thessaly, 41100 Larissa, Greece; (V.S.); (P.S.); (E.D.)
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Jellinger KA. Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks. Int J Mol Sci 2023; 25:498. [PMID: 38203667 PMCID: PMC10778722 DOI: 10.3390/ijms25010498] [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: 11/23/2023] [Revised: 12/11/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024] Open
Abstract
Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.
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Affiliation(s)
- Kurt A Jellinger
- Institute of Clinical Neurobiology, Alberichgasse 5/13, A-1150 Vienna, Austria
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Tsukita K, Sakamaki-Tsukita H, Kaiser S, Zhang L, Messa M, Serrano-Fernandez P, Takahashi R. High-Throughput CSF Proteomics and Machine Learning to Identify Proteomic Signatures for Parkinson Disease Development and Progression. Neurology 2023; 101:e1434-e1447. [PMID: 37586882 PMCID: PMC10573147 DOI: 10.1212/wnl.0000000000207725] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 05/30/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND AND OBJECTIVES This study aimed to identify CSF proteomic signatures characteristic of Parkinson disease (PD) and evaluate their clinical utility. METHODS This observational study used data from the Parkinson's Progression Markers Initiative (PPMI), which enrolled patients with PD, healthy controls (HCs), and non-PD participants carrying GBA1, LRRK2, and/or SNCA pathogenic variants (genetic prodromals) at international sites. Study participants were chosen from PPMI enrollees based on the availability of aptamer-based CSF proteomic data, quantifying 4,071 proteins, and classified as patients with PD without GBA1, LRRK2, and/or SNCA pathogenic variants (nongenetic PD), HCs, patients with PD carrying the aforementioned pathogenic variants (genetic PD), or genetic prodromals. Differentially expressed protein (DEP) analysis and the least absolute shrinkage and selection operator (LASSO) were applied to the data from nongenetic PD and HCs. Signatures characteristics of nongenetic PD were quantified as a PD proteomic score (PD-ProS), validated internally and then externally using data of 1,556 CSF proteins from the LRRK2 Cohort Consortium (LCC). We further tested the PD-ProS in genetic PD and genetic prodromals and examined associations with clinical progression. RESULTS Data from 279 patients with nongenetic PD (mean ± SD, age 62.0 ± 9.6 years; male 67.7%) and 141 HCs (age 60.5 ± 11.9 years; male 64.5%) were used for PD-ProS derivation. From 23 DEPs, LASSO determined weights of 14 DEPs for the PD-ProS (area under the curve [AUC] 0.83, 95% CI 0.78-0.87), validated in an independent internal validation cohort of 71 patients with nongenetic PD and 35 HCs (AUC 0.81, 95% CI 0.73-0.90). In the LCC, only 5 of the 14 DEPs were also measured. Notably, these 5 DEPs still distinguished 34 patients with nongenetic PD from 31 HCs with the same weights (AUC 0.75, 95% CI 0.63-0.87). Furthermore, the PD-ProS distinguished 258 patients with genetic PD from 365 genetic prodromals. Finally, regardless of genetic status, the PD-ProS independently predicted both cognitive and motor decline in PD (dementia, adjusted hazard ratio in the highest quintile [aHR-Q5] 2.8 [95% CI 1.6-5.0]; Hoehn and Yahr stage IV, aHR-Q5 2.1 [95% CI 1.1-4.0]). DISCUSSION By integrating high-throughput proteomics with machine learning, we identified PD-associated CSF proteomic signatures crucial for PD development and progression. TRIAL REGISTRATION INFORMATION ClinicalTrials.gov (NCT01176565). A link to the trial registry page is clinicaltrials.gov/ct2/show/NCT01141023. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that the CSF proteome contains clinically important information regarding the development and progression of Parkinson disease that can be deciphered by a combination of high-throughput proteomics and machine learning.
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Affiliation(s)
- Kazuto Tsukita
- From the Department of Neurology (K.T., H.S.-T., R.T.), Graduate School of Medicine, Kyoto University; Advanced Comprehensive Research Organization (K.T.), Teikyo University, Itabashi; Division of Sleep Medicine (K.T.), Kansai Electric Power Medical Research Institute, Osaka, Japan; Translational Medicine Department (S.K., P.S.-F.), Novartis Institutes for Biomedical Research, Basel, Switzerland; and Cardiovascular and Metabolism Department (L.Z.), and Neuroscience Department (M.M.), Novartis Institutes for Biomedical Research, Cambridge, MA.
| | - Haruhi Sakamaki-Tsukita
- From the Department of Neurology (K.T., H.S.-T., R.T.), Graduate School of Medicine, Kyoto University; Advanced Comprehensive Research Organization (K.T.), Teikyo University, Itabashi; Division of Sleep Medicine (K.T.), Kansai Electric Power Medical Research Institute, Osaka, Japan; Translational Medicine Department (S.K., P.S.-F.), Novartis Institutes for Biomedical Research, Basel, Switzerland; and Cardiovascular and Metabolism Department (L.Z.), and Neuroscience Department (M.M.), Novartis Institutes for Biomedical Research, Cambridge, MA
| | - Sergio Kaiser
- From the Department of Neurology (K.T., H.S.-T., R.T.), Graduate School of Medicine, Kyoto University; Advanced Comprehensive Research Organization (K.T.), Teikyo University, Itabashi; Division of Sleep Medicine (K.T.), Kansai Electric Power Medical Research Institute, Osaka, Japan; Translational Medicine Department (S.K., P.S.-F.), Novartis Institutes for Biomedical Research, Basel, Switzerland; and Cardiovascular and Metabolism Department (L.Z.), and Neuroscience Department (M.M.), Novartis Institutes for Biomedical Research, Cambridge, MA
| | - Luqing Zhang
- From the Department of Neurology (K.T., H.S.-T., R.T.), Graduate School of Medicine, Kyoto University; Advanced Comprehensive Research Organization (K.T.), Teikyo University, Itabashi; Division of Sleep Medicine (K.T.), Kansai Electric Power Medical Research Institute, Osaka, Japan; Translational Medicine Department (S.K., P.S.-F.), Novartis Institutes for Biomedical Research, Basel, Switzerland; and Cardiovascular and Metabolism Department (L.Z.), and Neuroscience Department (M.M.), Novartis Institutes for Biomedical Research, Cambridge, MA
| | - Mirko Messa
- From the Department of Neurology (K.T., H.S.-T., R.T.), Graduate School of Medicine, Kyoto University; Advanced Comprehensive Research Organization (K.T.), Teikyo University, Itabashi; Division of Sleep Medicine (K.T.), Kansai Electric Power Medical Research Institute, Osaka, Japan; Translational Medicine Department (S.K., P.S.-F.), Novartis Institutes for Biomedical Research, Basel, Switzerland; and Cardiovascular and Metabolism Department (L.Z.), and Neuroscience Department (M.M.), Novartis Institutes for Biomedical Research, Cambridge, MA
| | - Pablo Serrano-Fernandez
- From the Department of Neurology (K.T., H.S.-T., R.T.), Graduate School of Medicine, Kyoto University; Advanced Comprehensive Research Organization (K.T.), Teikyo University, Itabashi; Division of Sleep Medicine (K.T.), Kansai Electric Power Medical Research Institute, Osaka, Japan; Translational Medicine Department (S.K., P.S.-F.), Novartis Institutes for Biomedical Research, Basel, Switzerland; and Cardiovascular and Metabolism Department (L.Z.), and Neuroscience Department (M.M.), Novartis Institutes for Biomedical Research, Cambridge, MA
| | - Ryosuke Takahashi
- From the Department of Neurology (K.T., H.S.-T., R.T.), Graduate School of Medicine, Kyoto University; Advanced Comprehensive Research Organization (K.T.), Teikyo University, Itabashi; Division of Sleep Medicine (K.T.), Kansai Electric Power Medical Research Institute, Osaka, Japan; Translational Medicine Department (S.K., P.S.-F.), Novartis Institutes for Biomedical Research, Basel, Switzerland; and Cardiovascular and Metabolism Department (L.Z.), and Neuroscience Department (M.M.), Novartis Institutes for Biomedical Research, Cambridge, MA
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Xiao Y, Yang T, Shang H. The Impact of Motor-Cognitive Dual-Task Training on Physical and Cognitive Functions in Parkinson’s Disease. Brain Sci 2023; 13:brainsci13030437. [PMID: 36979247 PMCID: PMC10046387 DOI: 10.3390/brainsci13030437] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 02/15/2023] [Accepted: 02/20/2023] [Indexed: 03/08/2023] Open
Abstract
Rehabilitation is a high-potential approach to improving physical and cognitive functions in Parkinson’s disease (PD). Dual-task training innovatively combines motor and cognitive rehabilitation in a comprehensive module. Patients perform motor and cognitive tasks at the same time in dual-task training. The previous studies of dual-task training in PD had high heterogeneity and achieved controversial results. In the current review, we aim to summarize the current evidence of the effect of dual-task training on motor and cognitive functions in PD patients to support the clinical practice of dual-task training. In addition, we also discuss the current opinions regarding the mechanism underlying the interaction between motor and cognitive training. In conclusion, dual-task training is suitable for PD patients with varied disease duration to improve their motor function. Dual-task training can improve motor symptoms, single-task gait speed, single-task steep length, balance, and objective experience of freezing of gait in PD. The improvement in cognitive function after dual-task training is mild.
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Jang YO, Ahn HS, Dao TNT, Hong J, Shin W, Lim YM, Chung SJ, Lee JH, Liu H, Koo B, Kim MG, Kim K, Lee EJ, Shin Y. Magnetic transferrin nanoparticles (MTNs) assay as a novel isolation approach for exosomal biomarkers in neurological diseases. Biomater Res 2023; 27:12. [PMID: 36797805 PMCID: PMC9936675 DOI: 10.1186/s40824-023-00353-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 02/05/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND Brain-derived exosomes released into the blood are considered a liquid biopsy to investigate the pathophysiological state, reflecting the aberrant heterogeneous pathways of pathological progression of the brain in neurological diseases. Brain-derived blood exosomes provide promising prospects for the diagnosis of neurological diseases, with exciting possibilities for the early and sensitive diagnosis of such diseases. However, the capability of traditional exosome isolation assays to specifically isolate blood exosomes and to characterize the brain-derived blood exosomal proteins by high-throughput proteomics for clinical specimens from patients with neurological diseases cannot be assured. We report a magnetic transferrin nanoparticles (MTNs) assay, which combined transferrin and magnetic nanoparticles to isolate brain-derived blood exosomes from clinical samples. METHODS The principle of the MTNs assay is a ligand-receptor interaction through transferrin on MTNs and transferrin receptor on exosomes, and electrostatic interaction via positively charged MTNs and negatively charged exosomes to isolate brain-derived blood exosomes. In addition, the MTNs assay is simple and rapid (< 35 min) and does not require any large instrument. We confirmed that the MTNs assay accurately and efficiently isolated exosomes from serum samples of humans with neurodegenerative diseases, such as dementia, Parkinson's disease (PD), and multiple sclerosis (MS). Moreover, we isolated exosomes from serum samples of 30 patients with three distinct neurodegenerative diseases and performed unbiased proteomic analysis to explore the pilot value of brain-derived blood protein profiles as biomarkers. RESULTS Using comparative statistical analysis, we found 21 candidate protein biomarkers that were significantly different among three groups of neurodegenerative diseases. CONCLUSION The MTNs assay is a convenient approach for the specific and affordable isolation of extracellular vesicles from body fluids for minimally-invasive diagnosis of neurological diseases.
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Affiliation(s)
- Yoon Ok Jang
- grid.15444.300000 0004 0470 5454Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722 Republic of Korea
| | - Hee-Sung Ahn
- grid.413967.e0000 0001 0842 2126Department of Convergence Medicine, Asan Medical Center, Seoul, 05505 Republic of Korea
| | - Thuy Nguyen Thi Dao
- grid.15444.300000 0004 0470 5454Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722 Republic of Korea
| | - JeongYeon Hong
- grid.413967.e0000 0001 0842 2126Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505 Republic of Korea ,grid.267370.70000 0004 0533 4667Department of Biomedical Sciences, University of Ulsan College of Medicine, Seoul, 05505 Republic of Korea
| | - Wangyong Shin
- grid.413967.e0000 0001 0842 2126Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505 Republic of Korea
| | - Young-Min Lim
- grid.413967.e0000 0001 0842 2126Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505 Republic of Korea
| | - Sun Ju Chung
- grid.413967.e0000 0001 0842 2126Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505 Republic of Korea
| | - Jae-Hong Lee
- grid.413967.e0000 0001 0842 2126Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505 Republic of Korea
| | - Huifang Liu
- grid.15444.300000 0004 0470 5454Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722 Republic of Korea
| | - Bonhan Koo
- grid.15444.300000 0004 0470 5454Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722 Republic of Korea
| | - Myoung Gyu Kim
- grid.15444.300000 0004 0470 5454Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722 Republic of Korea
| | - Kyunggon Kim
- Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea. .,Department of Biomedical Sciences, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
| | - Eun-Jae Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
| | - Yong Shin
- Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, 03722, Republic of Korea.
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Patterson CG, Joslin E, Gil AB, Spigle W, Nemet T, Chahine L, Christiansen CL, Melanson E, Kohrt WM, Mancini M, Josbeno D, Balfany K, Griffith G, Dunlap MK, Lamotte G, Suttman E, Larson D, Branson C, McKee KE, Goelz L, Poon C, Tilley B, Kang UJ, Tansey MG, Luthra N, Tanner CM, Haus JM, Fantuzzi G, McFarland NR, Gonzalez-Latapi P, Foroud T, Motl R, Schwarzschild MA, Simuni T, Marek K, Naito A, Lungu C, Corcos DM. Study in Parkinson's disease of exercise phase 3 (SPARX3): study protocol for a randomized controlled trial. Trials 2022; 23:855. [PMID: 36203214 PMCID: PMC9535216 DOI: 10.1186/s13063-022-06703-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 09/01/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND To date, no medication has slowed the progression of Parkinson's disease (PD). Preclinical, epidemiological, and experimental data on humans all support many benefits of endurance exercise among persons with PD. The key question is whether there is a definitive additional benefit of exercising at high intensity, in terms of slowing disease progression, beyond the well-documented benefit of endurance training on a treadmill for fitness, gait, and functional mobility. This study will determine the efficacy of high-intensity endurance exercise as first-line therapy for persons diagnosed with PD within 3 years, and untreated with symptomatic therapy at baseline. METHODS This is a multicenter, randomized, evaluator-blinded study of endurance exercise training. The exercise intervention will be delivered by treadmill at 2 doses over 18 months: moderate intensity (4 days/week for 30 min per session at 60-65% maximum heart rate) and high intensity (4 days/week for 30 min per session at 80-85% maximum heart rate). We will randomize 370 participants and follow them at multiple time points for 24 months. The primary outcome is the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) motor score (Part III) with the primary analysis assessing the change in MDS-UPDRS motor score (Part III) over 12 months, or until initiation of symptomatic antiparkinsonian treatment if before 12 months. Secondary outcomes are striatal dopamine transporter binding, 6-min walk distance, number of daily steps, cognitive function, physical fitness, quality of life, time to initiate dopaminergic medication, circulating levels of C-reactive protein (CRP), and brain-derived neurotrophic factor (BDNF). Tertiary outcomes are walking stride length and turning velocity. DISCUSSION SPARX3 is a Phase 3 clinical trial designed to determine the efficacy of high-intensity, endurance treadmill exercise to slow the progression of PD as measured by the MDS-UPDRS motor score. Establishing whether high-intensity endurance treadmill exercise can slow the progression of PD would mark a significant breakthrough in treating PD. It would have a meaningful impact on the quality of life of people with PD, their caregivers and public health. TRIAL REGISTRATION ClinicalTrials.gov NCT04284436 . Registered on February 25, 2020.
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Affiliation(s)
- Charity G. Patterson
- Department of Physical Therapy, University of Pittsburgh, School of Health and Rehabilitation Sciences, 100 Technology Drive, Suite 500, Pittsburgh, PA 15219 USA
| | - Elizabeth Joslin
- Department of Physical Therapy and Human Science, Northwestern University, Feinberg School of Medicine, Suite 1100, 645 North Michigan Avenue, Chicago, IL 60305 USA
| | - Alexandra B. Gil
- Department of Physical Therapy, University of Pittsburgh, School of Health and Rehabilitation Sciences, 100 Technology Drive, Suite 500, Pittsburgh, PA 15219 USA
| | - Wendy Spigle
- Department of Physical Therapy, University of Pittsburgh, School of Health and Rehabilitation Sciences, 100 Technology Drive, Suite 500, Pittsburgh, PA 15219 USA
| | - Todd Nemet
- Department of Physical Therapy, University of Pittsburgh, School of Health and Rehabilitation Sciences, 100 Technology Drive, Suite 500, Pittsburgh, PA 15219 USA
| | - Lana Chahine
- Department of Neurology, University of Pittsburgh, School of Medicine, 3471 Fifth Avenue, Pittsburgh, PA 15213 USA
| | - Cory L. Christiansen
- Department of Physical Medicine & Rehabilitation, University of Colorado, School of Medicine, Aurora, CO 80217 USA
| | - Ed Melanson
- Division of Endocrinology, Metabolism and Diabetes, and Division of Geriatric Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO USA
- Eastern Colorado VA Health Care System, Geriatric Research Education and Clinical Center (GRECC), Denver, CO USA
| | - Wendy M. Kohrt
- Division of Geriatric Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO USA
- Eastern Colorado Geriatric Research, Education, and Clinical Center, Rocky Mountain Regional VAMC, Aurora, USA
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Road, Portland, OR 97219 USA
| | - Deborah Josbeno
- Department of Physical Therapy, University of Pittsburgh, School of Health and Rehabilitation Sciences, 100 Technology Drive, Suite 500, Pittsburgh, PA 15219 USA
| | - Katherine Balfany
- Department of Physical Medicine & Rehabilitation, University of Colorado, School of Medicine, Aurora, CO 80217 USA
| | - Garett Griffith
- Department of Physical Therapy and Human Science, Northwestern University, Feinberg School of Medicine, Suite 1100, 645 North Michigan Avenue, Chicago, IL 60305 USA
| | - Mac Kenzie Dunlap
- Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44195 USA
| | - Guillaume Lamotte
- Movement Disorders Division, Department of Neurology, University of Utah, 175 Medical Dr N, Salt Lake City, UT 84132 USA
| | - Erin Suttman
- Department of Physical Therapy & Athletic Training, University of Utah, 520 Wakara Way, Salt Lake City, UT 84115 USA
| | - Danielle Larson
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Suite 115, 710 N Lake Shore Drive, Chicago, IL 60611 USA
| | - Chantale Branson
- Morehouse School of Medicine, 720 Westview Dr SW, Atlanta, GA 30310 USA
| | - Kathleen E. McKee
- Neurosciences Clinical Program, Intermountain Healthcare, 5171 S Cottonwood Street, Suite 810, Murray, UT 84107 USA
| | - Li Goelz
- Department of Kinesiology and Nutrition, UIC College of Applied Health Sciences, 919 W Taylor Street, Chicago, IL 60612 USA
| | - Cynthia Poon
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Suite 115, 710 N Lake Shore Drive, Chicago, IL 60611 USA
| | - Barbara Tilley
- Department of Biostatistics and Data Science, University of Texas Health Science Center School of Public Health, 1200 Pressler Street E835, Houston, TX 77030 USA
| | - Un Jung Kang
- NYU Langone Health, NYU Grossman School of Medicine, 435 E 30th Street, Science Building 1305, New York, NY 10016 USA
| | - Malú Gámez Tansey
- Department of Neuroscience and Neurology, Normal Fixel Institute for Neurological Diseases and College of Medicine, University of Florida, 4911 Newell Road, Gainesville, FL 32610 USA
| | - Nijee Luthra
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, 1651 4th Street, San Francisco, CA 94158 USA
| | - Caroline M. Tanner
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, 1651 4th Street, San Francisco, CA 94158 USA
| | - Jacob M. Haus
- School of Kinesiology, University of Michigan, 830 N. University Ave, Ann Arbor, MI 48109 USA
| | - Giamila Fantuzzi
- Department of Kinesiology and Nutrition, UIC College of Applied Health Sciences, 919 W Taylor Street, Chicago, IL 60612 USA
| | - Nikolaus R. McFarland
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, College of Medicine, University of Florida, Gainesville, FL 32608 USA
| | - Paulina Gonzalez-Latapi
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Suite 115, 710 N Lake Shore Drive, Chicago, IL 60611 USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, 410 W. 10th Street, Indianapolis, IN 46220 USA
| | - Robert Motl
- Department of Kinesiology and Nutrition, UIC College of Applied Health Sciences, 919 W Taylor Street, Chicago, IL 60612 USA
| | - Michael A. Schwarzschild
- Mass General Institute for Neurodegenerative Disease, Massachusetts General Hospital, Rm 3002, 114 16th Street, Boston, MA 02129 USA
| | - Tanya Simuni
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Suite 115, 710 N Lake Shore Drive, Chicago, IL 60611 USA
| | - Kenneth Marek
- Institute for Neurodegenerative Disorders, 60 Temple St, New Haven, CT 06510 USA
| | - Anna Naito
- Parkinson’s Foundation 200 SE 1st Street Suite 800, Miami, FL 33131 USA
| | - Codrin Lungu
- National Institute of Neurological Disorders and Stroke, NIH, 6001 Executive Blvd, #2188, Rockville, MD 20852 USA
| | - Daniel M. Corcos
- Department of Physical Therapy and Human Science, Northwestern University, Feinberg School of Medicine, Suite 1100, 645 North Michigan Avenue, Chicago, IL 60305 USA
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Wang L, Wu P, Brown P, Zhang W, Liu F, Han Y, Zuo CT, Cheng W, Feng J. Association of Structural Measurements of Brain Reserve With Motor Progression in Patients With Parkinson Disease. Neurology 2022; 99:e977-e988. [PMID: 35667838 PMCID: PMC7613818 DOI: 10.1212/wnl.0000000000200814] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 04/19/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES To investigate the relationship between baseline structural measurements of brain reserve and clinical progression in Parkinson disease (PD). To further provide a possible underlying mechanism for structural measurements of brain reserve in PD, we combined functional and transcriptional data and investigated their relationship with progression-associated patterns derived from structural measurements and longitudinal clinical scores. METHODS This longitudinal study collected data from June 2010 to March 2019 from 2 datasets. The Parkinson's Progression Markers Initiative (PPMI) included controls and patients with newly diagnosed PD from 24 participating sites worldwide. Results were confirmed using data from the Huashan dataset (Shanghai, China), which included controls and patients with PD. Clinical symptoms were assessed with Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scores and Schwab & England activities of daily living (ADL). Both datasets were followed up to 5 years. Linear mixed-effects (LME) models were performed to examine whether changes in clinical scores over time differed as a function of brain structural measurements at baseline. RESULTS A total of 389 patients with PD (n = 346, age 61.3 ± 10.03, 35% female, PPMI dataset; n = 43, age 59.4 ± 7.3, 38.7% female, Huashan dataset) with T1-MRI and follow-up clinical assessments were included in this study. Results of LME models revealed significant interactions between baseline structural measurements of subcortical regions and time on longitudinal deterioration of clinical scores (MDS-UPDRS Part II, absolute β > 0.27; total MDS-UPDRS scores, absolute β > 1.05; postural instability-gait difficulty (PIGD) score, absolute β > 0.03; Schwab & England ADL, absolute β > 0.59; all p < 0.05, false discovery rate corrected). The interaction of baseline structural measurements of subcortical regions and time on longitudinal deterioration of the PIGD score was replicated using data from Huashan Hospital. Furthermore, the β-coefficients of these interactions recapitulated the spatial distribution of dopaminergic, metabolic, and structural changes between patients with PD and normal controls and the spatial distribution of expression of the α-synuclein gene (SNCA). DISCUSSION Patients with PD with greater brain resources (that is, higher deformation-based morphometry values) had greater compensatory capacity, which was associated with slower rates of clinical progression. This knowledge could be used to stratify and monitor patients for clinical trials.
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Affiliation(s)
- Linbo Wang
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Ping Wu
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Peter Brown
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Wei Zhang
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Fengtao Liu
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Yan Han
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Chuan-Tao Zuo
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Wei Cheng
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China
| | - Jianfeng Feng
- From the Institute of Science and Technology for Brain-inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; PET Center (P.W., C.-T.Z.), Huashan Hospital, Fudan University, Shanghai, China; Medical Research Council Brain Network Dynamics Unit (P.B.), and Nuffield Department of Clinical Neurosciences (P.B.), John Radcliffe Hospital, University of Oxford, United Kingdom; Department of Neurology (F.L., C.-T.Z.), Huashan Hospital North, Fudan University; Department of Neurology (Y.H.), Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine; Human Phenome Institute (C.-T.Z.), Fudan University; Zhangjiang Fudan International Innovation Center (W.C., J.F.), Shanghai, China; Department of Computer Science (W.C., J.F.), University of Warwick, Coventry, United Kingdom; and Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C., J.F.), Zhejiang Normal University, Jinhua, China.
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8
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Shen J, Amari N, Zack R, Skrinak RT, Unger TL, Posavi M, Tropea TF, Xie SX, Van Deerlin VM, Dewey RB, Weintraub D, Trojanowski JQ, Chen-Plotkin AS. Plasma MIA, CRP, and Albumin Predict Cognitive Decline in Parkinson's Disease. Ann Neurol 2022; 92:255-269. [PMID: 35593028 PMCID: PMC9329215 DOI: 10.1002/ana.26410] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 05/04/2022] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Using a multi-cohort, discovery-replication-validation design, we sought new plasma biomarkers that predict which individuals with Parkinson's disease (PD) will experience cognitive decline. METHODS In 108 discovery cohort PD individuals and 83 replication cohort PD individuals, we measured 940 plasma proteins on an aptamer-based platform. Using proteins associated with subsequent cognitive decline in both cohorts, we trained a logistic regression model to predict which patients with PD showed fast (> = 1 point drop/year on Montreal Cognitive Assessment [MoCA]) versus slow (< 1 point drop/year on MoCA) cognitive decline in the discovery cohort, testing it in the replication cohort. We developed alternate assays for the top 3 proteins and confirmed their ability to predict cognitive decline - defined by change in MoCA or development of incident mild cognitive impairment (MCI) or dementia - in a validation cohort of 118 individuals with PD. We investigated the top plasma biomarker for causal influence by Mendelian randomization (MR). RESULTS A model with only 3 proteins (melanoma inhibitory activity protein [MIA], C-reactive protein [CRP], and albumin) separated fast versus slow cognitive decline subgroups with an area under the curve (AUC) of 0.80 in the validation cohort. The individuals with PD in the validation cohort in the top quartile of risk for cognitive decline based on this model were 4.4 times more likely to develop incident MCI or dementia than those in the lowest quartile. Genotypes at MIA single nucleotide polymorphism (SNP) rs2233154 associated with MIA levels and cognitive decline, providing evidence for MIA's causal influence. CONCLUSIONS An easily obtained plasma-based predictor identifies individuals with PD at risk for cognitive decline. MIA may participate causally in development of cognitive decline. ANN NEUROL 2022.
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Affiliation(s)
- Junchao Shen
- Departments of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Noor Amari
- Departments of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Rebecca Zack
- Departments of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - R Tyler Skrinak
- Departments of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Travis L Unger
- Departments of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Marijan Posavi
- Departments of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Thomas F Tropea
- Departments of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Sharon X Xie
- Departments of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Vivianna M Van Deerlin
- Departments of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Richard B Dewey
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Daniel Weintraub
- Departments of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.,Parkinson's Disease Research, Education and Clinical Center (PADRECC), Philadelphia Veterans Affairs Medical Center, Philadelphia, PA
| | - John Q Trojanowski
- Departments of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Alice S Chen-Plotkin
- Departments of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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9
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Tsukita K, Sakamaki-Tsukita H, Takahashi R. Long-term Effect of Regular Physical Activity and Exercise Habits in Patients With Early Parkinson Disease. Neurology 2022; 98:e859-e871. [PMID: 35022304 PMCID: PMC8883509 DOI: 10.1212/wnl.0000000000013218] [Citation(s) in RCA: 70] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 11/30/2021] [Indexed: 12/05/2022] Open
Abstract
Background and Objectives Owing to the lack of long-term observations or comprehensive adjustment for confounding factors, reliable conclusions regarding long-term effects of exercise and regular physical activity in Parkinson disease (PD) have yet to be drawn. Here, using data from the Parkinson's Progression Markers Initiative study that includes longitudinal and comprehensive evaluations of many clinical parameters, we examined the long-term effects of regular physical activity and exercise habits on the course of PD. Methods In this retrospective, observational cohort study, we primarily used the multivariate linear mixed-effects models to analyze the interaction effects of their regular physical activity and moderate to vigorous exercise levels, measured with the Physical Activity Scale for the Elderly questionnaire, on the progression of clinical parameters, after adjusting for age, sex, levodopa equivalent dose, and disease duration. We also calculated bootstrapping 95% confidence intervals (CIs) and conducted sensitivity analyses using the multiple imputation method and subgroup analyses using propensity score matching to match for all baseline background factors. Results Two hundred thirty-seven patients with early PD (median [interquartile range] age, 63.0 [56.0–70.0] years, male 69.2%, follow-up duration 5.0 [4.0–6.0] years) were included. Regular physical activity and moderate to vigorous exercise levels at baseline did not significantly affect the subsequent clinical progression of PD. However, average regular overall physical activity levels over time were significantly associated with slower deterioration of postural and gait stability (standardized fixed-effects coefficients of the interaction term [βinteraction] = −0.10 [95% CI −0.14 to −0.06]), activities of daily living (βinteraction = 0.08 [95% CI 0.04–0.12]), and processing speed (βinteraction = 0.05 [95% CI 0.03–0.08]) in patients with PD. Moderate to vigorous exercise levels were preferentially associated with slower decline of postural and gait stability (βinteraction = −0.09 [95% CI −0.13 to −0.05]), and work-related activity levels were primarily associated with slower deterioration of processing speed (βinteraction = 0.07 [95% CI 0.04–0.09]). Multiple imputation and propensity score matching confirmed the robustness of our results. Discussion In the long term, the maintenance of high regular physical activity levels and exercise habits was robustly associated with better clinical course of PD, with each type of physical activity having different effects. Trial Registration Information ClinicalTrials.gov Identifier: NCT01176565. Classification of Evidence This study provides Class II evidence that sustained increase in overall regular physical activity levels in patients with early PD was associated with slower decline of several clinical parameters.
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Affiliation(s)
- Kazuto Tsukita
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan .,Division of Sleep Medicine, Kansai Electric Power Medical Research Institute, Osaka, Japan.,Laboratory of Barriology and Cell Biology, Graduate School of Frontier Biosciences, Osaka University, Suita, Japan
| | | | - Ryosuke Takahashi
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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10
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Yang T, Liu Y, Li J, Xu H, Li S, Xiong L, Wang T. Advances in clinical basic research: Performance, treatments, and mechanisms of Parkinson disease. IBRAIN 2021; 7:362-378. [PMID: 37786563 PMCID: PMC10529016 DOI: 10.1002/ibra.12011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/05/2021] [Accepted: 12/05/2021] [Indexed: 02/05/2023]
Abstract
The loss of neuronal in the substantia nigra of the elderly contributes to striatal damage and plays a critical part in the common forms of neurodegenerative diseases such as Parkinson disease (PD). The deficit of dopamine is one of the most familiar neuropathological features of PD as well as α-Synuclein aggregation. The peripheral autonomic nervous system is also affected negatively during the course of the disease, although the subsistent of dyskinesias and else major motor characteristic deficits take significant role in the diagnostic methods during clinical practice, which is related to a number of non-motor symptoms that might increase aggregate risks. Multiple pathways and mechanisms are involved in the molecular pathogenesis: α-Synuclein, neuronal homeostasis, mitochondrial function, oxidative stress, as well as neuroinflammation. Investigations in the last few years for diagnostic biomarkers used neuroimaging, including single photon emission computed tomography as well as cutting-edge magnetic resonance imaging techniques, which has been presented to facilitate discrepant diagnosis. Pharmacological treatment is also important and efficient in equal measure. In addition to reliance on striatal dopamine replacement therapy, many solutions that are used for motor or nonmotor symptoms in these patients are available.
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Affiliation(s)
- Ting‐Ting Yang
- Department of AnesthesiologyZunyi Medical UniversityZunyiGuizhouChina
| | - Yu‐Cong Liu
- Department of AnesthesiologyZunyi Medical UniversityZunyiGuizhouChina
| | - Jing Li
- Department of AnesthesiologyZunyi Medical UniversityZunyiGuizhouChina
| | - Hui‐Chan Xu
- Department of AnesthesiologyZunyi Medical UniversityZunyiGuizhouChina
| | - Shun‐Lian Li
- Department of AnesthesiologyZunyi Medical UniversityZunyiGuizhouChina
| | - Liu‐Lin Xiong
- Department of AnesthesiologyAffiliated Hospital of Zunyi Medical UniversityZunyiGuizhouChina
| | - Ting‐Hua Wang
- Department of Anesthesiology, Translational Neuroscience Center, Institute of Neurological Disease, West China HospitalSichuan UniversityChengduChina
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11
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Tsukita K, Sakamaki-Tsukita H, Takahashi R. Lower Circulating Lymphocyte Count Predicts ApoE ε4-Related Cognitive Decline in Parkinson's Disease. Mov Disord 2021; 36:2969-2971. [PMID: 34647353 PMCID: PMC9293429 DOI: 10.1002/mds.28799] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 08/26/2021] [Indexed: 01/13/2023] Open
Affiliation(s)
- Kazuto Tsukita
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Advanced Comprehensive Research Organization, Teikyo University, Tokyo, Japan.,Division of Sleep Medicine, Kansai Electric Power Medical Research Institute, Osaka, Japan
| | | | - Ryosuke Takahashi
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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12
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Raber J, Darweesh SKL, Savica R. Physical Activity May Reduce Apolipoprotein E4-Associated Cognitive Decline in Parkinson Disease. Neurology 2021; 96:877-878. [PMID: 33790042 DOI: 10.1212/wnl.0000000000011851] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Affiliation(s)
- Jacob Raber
- From the Departments of Behavioral Neuroscience, Neurology, and Radiation Medicine (J.R.), Division of Neuroscience, ONPRC, Oregon Health & Science University, Portland; Department of Neurology (S.K.L.D.), Radboud University Medical Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands; and Department of Neurology and Health Science Research (R.S.), Mayo Clinic, Rochester, MN.
| | - Sirwan K L Darweesh
- From the Departments of Behavioral Neuroscience, Neurology, and Radiation Medicine (J.R.), Division of Neuroscience, ONPRC, Oregon Health & Science University, Portland; Department of Neurology (S.K.L.D.), Radboud University Medical Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands; and Department of Neurology and Health Science Research (R.S.), Mayo Clinic, Rochester, MN
| | - Rodolfo Savica
- From the Departments of Behavioral Neuroscience, Neurology, and Radiation Medicine (J.R.), Division of Neuroscience, ONPRC, Oregon Health & Science University, Portland; Department of Neurology (S.K.L.D.), Radboud University Medical Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands; and Department of Neurology and Health Science Research (R.S.), Mayo Clinic, Rochester, MN
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