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Inguanzo A, Mohanty R, Poulakis K, Ferreira D, Segura B, Albrecht F, Muehlboeck JS, Granberg T, Sjöström H, Svenningsson P, Franzén E, Junqué C, Westman E. MRI subtypes in Parkinson's disease across diverse populations and clustering approaches. NPJ Parkinsons Dis 2024; 10:159. [PMID: 39152153 PMCID: PMC11329719 DOI: 10.1038/s41531-024-00759-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 07/22/2024] [Indexed: 08/19/2024] Open
Abstract
Parkinson's disease (PD) is clinically heterogeneous, which suggests the existence of subtypes; however, there has been no consensus regarding their characteristics. This study included 633 PD individuals across distinct cohorts: unmedicated de novo PD, medicated PD, mild-moderate PD, and a cohort based on diagnostic work-up in clinical practice. Additionally, 233 controls were included. Clustering based on cortical and subcortical gray matter measures was conducted with and without adjusting for global atrophy in the entire PD sample and validated within each cohort. Subtypes were characterized using baseline and longitudinal demographic and clinical data. Unadjusted results identified three clusters showing a gradient of neurodegeneration and symptom severity across the entire sample and the individual cohorts. When adjusting for global atrophy eight clusters were identified in the entire sample, lacking consistency in individual cohorts. This study identified atrophy-based subtypes in PD, emphasizing the significant impact of global atrophy on subtype number, patterns, and interpretation in cross-sectional analyses.
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Affiliation(s)
- Anna Inguanzo
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden.
- Medical Psychology Unit, Department of Medicine, Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain.
| | - Rosaleena Mohanty
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
| | - Konstantinos Poulakis
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
- Facultad de Ciencias de la Salud. Universidad Fernando Pessoa Canarias, Las Palmas, Spain
| | - Barbara Segura
- Medical Psychology Unit, Department of Medicine, Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain
- Fundació de Recerca Clínic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (FRCB-IDIBAPS), Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Barcelona, Catalonia, Spain
| | - Franziska Albrecht
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Karolinska University Hospital, Women's Health and Allied Health Professionals Theme, Medical unit Occupational Therapy & Physiotherapy, Stockholm, Sweden
| | - J-Sebastian Muehlboeck
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden
| | - Tobias Granberg
- Division of Neuro, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Henrik Sjöström
- Division of Neuro, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Neurology, Academic Specialist Center, Stockholm, Sweden
| | - Per Svenningsson
- Division of Neuro, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Center for Neurology, Academic Specialist Center, Stockholm, Sweden
- Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Erika Franzén
- Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Karolinska University Hospital, Women's Health and Allied Health Professionals Theme, Medical unit Occupational Therapy & Physiotherapy, Stockholm, Sweden
| | - Carme Junqué
- Medical Psychology Unit, Department of Medicine, Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain
- Fundació de Recerca Clínic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (FRCB-IDIBAPS), Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas, Barcelona, Catalonia, Spain
| | - Eric Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, Sweden.
- Department of Neuroimaging, Center for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK.
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Zheng L, Zhou C, Mao C, Xie C, You J, Cheng W, Liu C, Huang P, Guan X, Guo T, Wu J, Luo Y, Xu X, Zhang B, Zhang M, Wang L, Feng J. Contrastive machine learning reveals Parkinson's disease specific features associated with disease severity and progression. Commun Biol 2024; 7:954. [PMID: 39112797 PMCID: PMC11306336 DOI: 10.1038/s42003-024-06648-x] [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: 12/12/2023] [Accepted: 07/29/2024] [Indexed: 08/10/2024] Open
Abstract
Parkinson's disease (PD) exhibits heterogeneity in terms of symptoms and prognosis, likely due to diverse neuroanatomical alterations. This study employs a contrastive deep learning approach to analyze Magnetic Resonance Imaging (MRI) data from 932 PD patients and 366 controls, aiming to disentangle PD-specific neuroanatomical alterations. The results reveal that these neuroanatomical alterations in PD are correlated with individual differences in dopamine transporter binding deficit, neurodegeneration biomarkers, and clinical severity and progression. The correlation with clinical severity is verified in an external cohort. Notably, certain proteins in the cerebrospinal fluid are strongly associated with PD-specific features, particularly those involved in the immune function. The most notable neuroanatomical alterations are observed in both subcortical and temporal regions. Our findings provide deeper insights into the patterns of brain atrophy in PD and potential underlying molecular mechanisms, paving the way for earlier patient stratification and the development of treatments to slow down neurodegeneration.
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Affiliation(s)
- Liping Zheng
- Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chengjie Mao
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Jia You
- Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China
| | - Chunfeng Liu
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoujun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yajun Luo
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Linbo Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- School of Data Science, Fudan University, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, UK.
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Kikuya A, Tsukita K, Sawamura M, Yoshimura K, Takahashi R. Distinct Clinical Implications of Patient- Versus Clinician-Rated Motor Symptoms in Parkinson's Disease. Mov Disord 2024. [PMID: 39092513 DOI: 10.1002/mds.29962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 07/10/2024] [Accepted: 07/17/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND Patient-rated motor symptoms (PRMS) and clinician-rated motor symptoms (CRMS) often differ in Parkinson's disease (PD). OBJECTIVE Our goal was to investigate the determinants and clinical implications of PRMS compared with CRMS in PD. METHODS This retrospective, observational cohort study analyzed the cross-sectional associations and longitudinal impacts of PRMS as assessed by the Movement Disorders Society-sponsored Unified PD Rating Scale (MDS-UPDRS) part 2, while controlling for CRMS measured by MDS-UPDRS part 3. Longitudinal analyses used Cox proportional hazards models and multiple linear mixed-effects random intercepts/slope models, adjusting for many clinical predictors. We conducted propensity score matching (PSM) to reinforce our analyses' robustness and surface-based morphometry to investigate neural correlates. RESULTS We enrolled 442 patients with early-stage PD. At baseline, regardless of CRMS, PRMS were associated with the severity of postural instability and gait disturbance (PIGD). Notably, PRMS independently and more accurately predicted faster long-term deterioration in motor function than CRMS (Hoehn and Yahr 4, adjusted hazard ratio per +1 point = 1.19 [95% confidence intervals, 1.08-1.32]), particularly in PIGD (PIGD subscore, β-interaction = 0.052 [95% confidence intervals, 0.018-0.086]). PSM confirmed these findings' robustness. Surface-based morphometry suggested that enhanced sensory processing was distinctively associated with PRMS. CONCLUSIONS In early-stage PD, PRMS weighed different aspects of symptoms and more effectively predicted motor deterioration compared to CRMS, with distinctive brain structural characteristics. The superior sensitivity of PRMS to subtle declines in drug-refractory symptoms like PIGD likely underlie our results, highlighting the importance of understanding the differential clinical implications of PRMS to prevent long-term motor deterioration. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Akihiro Kikuya
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - 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
| | - Masanori Sawamura
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kenji Yoshimura
- Department of Neurology, Osaka City General Hospital, Osaka, Japan
| | - Ryosuke Takahashi
- Department of Neurology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Arnaldi D, Iranzo A, Nobili F, Postuma RB, Videnovic A. Developing disease-modifying interventions in idiopathic REM sleep behavior disorder and early synucleinopathy. Parkinsonism Relat Disord 2024; 125:107042. [PMID: 38943771 DOI: 10.1016/j.parkreldis.2024.107042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 07/01/2024]
Abstract
Alpha-synucleinopathies are prevalent neurological disorders that cause significant disability, leading to progressive clinical deterioration that is currently managed solely through symptomatic treatment. Efforts to evaluate disease-modifying therapies during the established stage of the disease have not yielded positive outcomes in terms of clinical or imaging efficacy endpoints. However, alpha-synucleinopathies have a long prodromal phase that presents a promising opportunity for intervention with disease-modifying therapies. The presence of polysomnography-confirmed REM sleep behavior disorder (RBD) is the most reliable risk factor for identifying individuals in the prodromal stage of alpha-synucleinopathy. This paper discusses the rationale behind targeting idiopathic/isolated RBD in disease-modifying trials and outlines possible study designs, including strategies for patient stratification, selection of biomarkers to assess disease progression and patient eligibility, as well as the identification of suitable endpoints. Additionally, the potential targets for disease-modifying treatment in alpha-synucleinopathies are summarized.
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Affiliation(s)
- Dario Arnaldi
- Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy; Neurofisiopatologia, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
| | - Alex Iranzo
- Neurology Service, Sleep Disorder Centre, Hospital Clinic Barcelona, Universitat de Barcelona, IDIBAPS, CIBERNED: CB06/05/0018-ISCIII, Barcelona, Spain
| | - Flavio Nobili
- Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy
| | - Ronald B Postuma
- Department of Neurology, McGill University, Montreal Neurological Institute, Montreal, Canada; Centre d'Études Avancées en Médecine du Sommeil, Hôpital du Sacré-Cœur de Montréal, Montréal, Canada
| | - Aleksandar Videnovic
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Fujita H, Ogaki K, Shiina T, Sakuramoto H, Nozawa N, Suzuki K. Impact of autonomic symptoms on the clinical course of Parkinson's disease. Neurol Sci 2024; 45:3799-3807. [PMID: 38400889 DOI: 10.1007/s10072-024-07422-x] [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: 12/29/2023] [Accepted: 02/20/2024] [Indexed: 02/26/2024]
Abstract
PURPOSE Patients with Parkinson's disease (PD) exhibit various degrees of autonomic symptoms, which may be associated with Lewy body pathology distributed extensively in the autonomic nervous system. We hypothesized that the severity of autonomic symptoms reflects the severity of PD-related pathology, resulting in poor outcomes. The purpose of this study was to evaluate the impact of autonomic symptoms on PD progression. METHODS We conducted a follow-up study among consecutive patients with PD at Dokkyo Medical University Hospital. Patients underwent comprehensive baseline evaluations and were classified into high and low autonomic symptom groups using the Scale for Outcomes in Parkinson's Disease-Autonomic (SCOPA-AUT). The Kaplan‒Meier survival curves were used to analyze the time to discontinuation of their visits because of PD-related endpoints and to evaluate the association with high SCOPA-AUT scores. RESULTS Of the 101 patients, 74 (73%) met the inclusion criteria. During the follow-up period (mean 1654 days), 22/74 patients reached PD-related endpoints (death, 4; hospitalization, 9; nursing home institutionalization, 9). PD patients with high SCOPA-AUT scores reached the endpoints faster than those with low SCOPA-AUT scores. A high SCOPA-AUT score, including gastrointestinal, urinary, and thermoregulation domains; high motor symptom scores; and low specific binding ratios (SBRs) on 123I FP-CIT-SPECT (DAT-SPECT) were associated with reaching PD-related endpoints. A high SCOPA-AUT score was associated with reaching the endpoints even after adjustment for covariates. CONCLUSIONS Patients with high autonomic symptom scores had a greater risk of reaching PD-related endpoints than patients with low autonomic symptom scores.
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Affiliation(s)
- Hiroaki Fujita
- Department of Neurology, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsuga, Tochigi, 321-0293, Japan.
| | - Keitaro Ogaki
- Department of Neurology, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsuga, Tochigi, 321-0293, Japan
| | - Tomohiko Shiina
- Department of Neurology, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsuga, Tochigi, 321-0293, Japan
| | - Hirotaka Sakuramoto
- Department of Neurology, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsuga, Tochigi, 321-0293, Japan
| | - Narihiro Nozawa
- Department of Neurology, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsuga, Tochigi, 321-0293, Japan
| | - Keisuke Suzuki
- Department of Neurology, Dokkyo Medical University, 880 Kitakobayashi, Mibu, Shimotsuga, Tochigi, 321-0293, Japan
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Yuan X, Yu Q, Liu Y, Chen J, Gao J, Liu Y, Song R, Zhang Y, Hou Z. Microstructural alterations in white matter and related neurobiology based on the new clinical subtypes of Parkinson's disease. Front Neurosci 2024; 18:1439443. [PMID: 39148522 PMCID: PMC11324559 DOI: 10.3389/fnins.2024.1439443] [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: 05/28/2024] [Accepted: 07/15/2024] [Indexed: 08/17/2024] Open
Abstract
Background and objectives The advent of new clinical subtyping systems for Parkinson's disease (PD) has led to the classification of patients into distinct groups: mild motor predominant (PD-MMP), intermediate (PD-IM), and diffuse malignant (PD-DM). Our goal was to evaluate the efficacy of diffusion tensor imaging (DTI) in the early diagnosis, assessment of clinical progression, and prediction of prognosis of these PD subtypes. Additionally, we attempted to understand the pathological mechanisms behind white matter damage using single-photon emission computed tomography (SPECT) and cerebrospinal fluid (CSF) analyses. Methods We classified 135 de novo PD patients based on new clinical criteria and followed them up after 1 year, along with 45 healthy controls (HCs). We utilized tract-based spatial statistics to assess the microstructural changes of white matter at baseline and employed multiple linear regression to examine the associations between DTI metrics and clinical data at baseline and after follow-up. Results Compared to HCs, patients with the PD-DM subtype demonstrated reduced fractional anisotropy (FA), increased axial diffusivity (AD), and elevated radial diffusivity (RD) at baseline. The FA and RD values correlated with the severity of motor symptoms, with RD also linked to cognitive performance. Changes in FA over time were found to be in sync with changes in motor scores and global composite outcome measures. Furthermore, baseline AD values and their rate of change were related to alterations in semantic verbal fluency. We also discovered the relationship between FA values and the levels of α-synuclein and β-amyloid. Reduced dopamine transporter uptake in the left putamen correlated with RD values in superficial white matter, motor symptoms, and autonomic dysfunction at baseline as well as cognitive impairments after 1 year. Conclusions The PD-DM subtype is characterized by severe clinical symptoms and a faster progression when compared to the other subtypes. DTI, a well-established technique, facilitates the early identification of white matter damage, elucidates the pathophysiological mechanisms of disease progression, and predicts cognitively related outcomes. The results of SPECT and CSF analyses can be used to explain the specific pattern of white matter damage in patients with the PD-DM subtype.
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Affiliation(s)
- Xiaorong Yuan
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Qiaowen Yu
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Medical Imaging, Shandong Provincial Hospital, Jinan, Shandong, China
| | - Yanyan Liu
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Jinge Chen
- Department of Radiology, Shandong Mental Health Center, Jinan, Shandong, China
| | - Jie Gao
- Department of Medical Imaging, Shandong Provincial Third Hospital, Jinan, Shandong, China
| | - Yujia Liu
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Ruxi Song
- Department of Radiology, Binzhou Medical University Hospital, Binzhou, China
| | - Yingzhi Zhang
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Zhongyu Hou
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Department of Medical Imaging, Shandong Provincial Hospital, Jinan, Shandong, China
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Yoo HS, Kim HK, Lee HS, Yoon SH, Na HK, Kang SW, Lee JH, Ryu YH, Lyoo CH. Predictors associated with the rate of progression of nigrostriatal degeneration in Parkinson's disease. J Neurol 2024; 271:5213-5222. [PMID: 38839638 DOI: 10.1007/s00415-024-12477-z] [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: 12/20/2023] [Revised: 04/20/2024] [Accepted: 05/23/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND Parkinson's disease (PD) manifests as a wide variety of clinical phenotypes and its progression varies greatly. However, the factors associated with different disease progression remain largely unknown. METHODS In this retrospective cohort study, we enrolled 113 patients who underwent 18F-FP-CIT PET scan twice. Given the negative exponential progression pattern of dopamine loss in PD, we applied the natural logarithm to the specific binding ratio (SBR) of two consecutive 18F-FP-CIT PET scans and conducted linear mixed model to calculate individual slope to define the progression rate of nigrostriatal degeneration. We investigated the clinical and dopamine transporter (DAT) availability patterns associated with the progression rate of dopamine depletion in each striatal sub-region. RESULTS More symmetric parkinsonism, the presence of dyslipidemia, lower K-MMSE total score, and lower anteroposterior gradient of the mean putaminal SBR were associated with faster progression rate of dopamine depletion in the caudate nucleus. More symmetric parkinsonism and lower anteroposterior gradient of the mean putaminal SBR were associated with faster depletion of dopamine in the anterior putamen. Older age at onset, more symmetric parkinsonism, the presence of dyslipidemia, and lower anteroposterior gradient of the mean putaminal SBR were associated with faster progression rate of dopamine depletion in the posterior putamen. Lower striatal mean SBR predicted the development of LID, while lower mean SBR in the caudate nuclei predicted the development of dementia. DISCUSSION Our results suggest that the evaluation of baseline clinical features and patterns of DAT availability can predict the progression of PD and its prognosis.
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Affiliation(s)
- Han Soo Yoo
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, 20 Eonjuro 63-gil, Gangnam-gu, Seoul, South Korea
| | - Han-Kyeol Kim
- Department of Neurology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Wonju, South Korea
| | - Hye Sun Lee
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, South Korea
| | - So Hoon Yoon
- Department of Neurology, International St. Mary's Hospital, Catholic Kwandong University College of Medicine, Incheon, South Korea
| | - Han Kyu Na
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, 20 Eonjuro 63-gil, Gangnam-gu, Seoul, South Korea
| | - Sung Woo Kang
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, 20 Eonjuro 63-gil, Gangnam-gu, Seoul, South Korea
| | - Jae-Hoon Lee
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, 20 Eonjuro 63-gil, Gangnam-gu, Seoul, South Korea
| | - Young Hoon Ryu
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, 20 Eonjuro 63-gil, Gangnam-gu, Seoul, South Korea.
| | - Chul Hyoung Lyoo
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, 20 Eonjuro 63-gil, Gangnam-gu, Seoul, South Korea.
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Postuma RB, Weintraub D, Simuni T, Rodríguez‐Violante M, Leentjens AF, Hu MT, Espay AJ, Erro R, Dujardin K, Bohnen NI, Berg D, Mestre TA, Marras C. Anticipating Tomorrow: Tailoring Parkinson's Symptomatic Therapy Using Predictors of Outcome. Mov Disord Clin Pract 2024; 11:983-991. [PMID: 38817000 PMCID: PMC11329576 DOI: 10.1002/mdc3.14089] [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: 10/02/2023] [Accepted: 05/01/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Although research into Parkinson's disease (PD) subtypes and outcome predictions has continued to advance, recommendations for using outcome prediction to guide current treatment decisions remain sparse. OBJECTIVES To provide expert opinion-based recommendations for individually tailored PD symptomatic treatment based on knowledge of risk prediction and subtypes. METHODS Using a modified Delphi approach, members of the Movement Disorders Society (MDS) Task Force on PD subtypes generated a series of general recommendations around the question: "Using what you know about genetic/biological/clinical subtypes (or any individual-level predictors of outcome), what advice would you give for selecting symptomatic treatments for an individual patient now, based on what their subtype or individual characteristics predict about their future disease course?" After four iterations and revisions, those recommendations with over 75% endorsement were adopted. RESULTS A total of 19 recommendations were endorsed by a group of 13 panelists. The recommendations primarily centered around two themes: (1) incorporating future risk of cognitive impairment into current treatment plans; and (2) identifying future symptom clusters that might be forestalled with a single medication. CONCLUSIONS These recommendations provide clinicians with a framework for integrating future outcomes into patient-specific treatment choices. They are not prescriptive guidelines, but adaptable suggestions, which should be tailored to each individual. They are to be considered as a first step of a process that will continue to evolve as additional stakeholders provide new insights and as new information becomes available. As individualized risk prediction advances, the path to better tailored treatment regimens will become clearer.
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Affiliation(s)
- Ronald B. Postuma
- Department of NeurologyMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Daniel Weintraub
- Departments of Psychiatry and Neurology, Perelman School of Medicine at the University of Pennsylvania; Parkinson's Disease Research, Education and Clinical Center (PADRECC)Philadelphia Veterans Affairs Medical CenterPhiladelphiaPennsylvaniaUSA
| | - Tanya Simuni
- Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
| | | | - Albert F.G. Leentjens
- Department of PsychiatryMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Michele T. Hu
- Nuffield Department of Clinical Neurosciences, Neurology DepartmentOxford University and John Radcliffe HospitalOxfordUnited Kingdom
| | - Alberto J. Espay
- James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of NeurologyUniversity of CincinnatiCincinnatiOhioUSA
| | - Roberto Erro
- Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”, Neuroscience SectionUniversity of SalernoBaronissiItaly
| | - Kathy Dujardin
- Neurology and Movement Disorders DepartmentUniversity of Lille, Inserm, Lille Neurosciences and Cognition, CHU‐LilleLilleFrance
| | - Nicolaas I. Bohnen
- Departments of Radiology and NeurologyUniversity of Michigan, University of Michigan Udall Center, Ann Arbor VAMCAnn ArborMichiganUSA
| | - Daniela Berg
- Department of NeurologyChristian‐Albrechts‐UniversityKielGermany
| | - Tiago A. Mestre
- Division of Neurology, Department of MedicineUniversity of Ottawa, The University of Ottawa Brain and Research InstituteOttawaOntarioCanada
- Parkinson's Disease and Movement Disorders ClinicThe Ottawa Hospital, The Ottawa Hospital Research InstituteOttawaOntarioCanada
| | - Connie Marras
- Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders ClinicToronto Western Hospital, University Health NetworkTorontoOntarioCanada
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9
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Chen X, He C, Ma J, Yang R, Qi Q, Gao Z, Du T, Zhang P, Li Y, Cai M, Zhang Y. Motor progression trajectories and risk of mild cognitive impairment in Parkinson's disease: A latent class trajectory model from PPMI cohort. CNS Neurosci Ther 2024; 30:e14918. [PMID: 39129413 PMCID: PMC11317696 DOI: 10.1111/cns.14918] [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: 01/02/2024] [Revised: 06/14/2024] [Accepted: 07/30/2024] [Indexed: 08/13/2024] Open
Abstract
AIMS Rare studies have investigated the association between heterogeneity of motor progression and risk of early cognitive impairment in Parkinson's disease (PD). In this study, we aim to identify distinct trajectories of motor progression longitudinally and investigate their impact on predicting mild cognitive impairment (MCI). METHODS A 5-year cohort including 415 PD patients at baseline was collected from the Parkinson's Progression Markers Initiative. The severity of motor symptoms was evaluated using the Movement Disorder Society Unified Parkinson's Disease Rating Scale part III. The latent class trajectory model and nonlinear mixed-effects model were used to analyze and delineate the longitudinal changes in motor symptoms. Propensity score matching (PSM) was used to minimize the impact of potential confounders. Cox proportional hazard models were applied to calculate hazard ratios for MCI, and a Kaplan-Meier curve was generated using the occurrence of MCI during the follow-up as the time-to-event. RESULTS Two latent trajectories were identified: a mild and remitting motor symptoms class (Class 1, 33.01%) and a severe and progressive motor symptom class (Class 2, 66.99%). Patients in Class 2 initially exhibited severe motor symptoms that worsened progressively despite receiving anti-PD medications. In comparison, patients in Class 1 exhibited milder symptoms that improved following drug therapy and a slower progression. During a 5-year follow-up, patients in Class 2 showed a higher risk of developing MCI compared to those in Class 1 before PSM (Log-Rank 28.58, p < 0.001) and after PSM (Log-Rank 8.20, p = 0.004). CONCLUSIONS PD patients with severe and progressive motor symptoms are more likely to develop MCI than those with mild and stable motor symptoms.
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Affiliation(s)
- Xi Chen
- Shantou University Medical CollegeShantouGuangdong ProvinceChina
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong ProvinceChina
| | - Chentao He
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong ProvinceChina
| | - Jianrui Ma
- Shantou University Medical CollegeShantouGuangdong ProvinceChina
| | - Rui Yang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong ProvinceChina
- School of MedicineSouth China University of TechnologyGuangzhouChina
| | - Qi Qi
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong ProvinceChina
- Guangdong Cardiovascular InstituteGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
| | - Ziqi Gao
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong ProvinceChina
- School of MedicineSouth China University of TechnologyGuangzhouChina
| | - Tingyue Du
- Shantou University Medical CollegeShantouGuangdong ProvinceChina
| | - Piao Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong ProvinceChina
| | - Yan Li
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong ProvinceChina
| | - Mengfei Cai
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong ProvinceChina
- Guangdong Cardiovascular InstituteGuangdong Provincial People's Hospital, Guangdong Academy of Medical SciencesGuangzhouChina
| | - Yuhu Zhang
- Shantou University Medical CollegeShantouGuangdong ProvinceChina
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong ProvinceChina
- Guangzhou Key Laboratory of Diagnosis and Treatment for Neurodegenerative DiseasesGuangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)GuangzhouChina
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10
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Zhang P, Wan X, Jiang J, Liu Y, Wang D, Ai K, Liu G, Zhang X, Zhang J. A causal effect study of cortical morphology and related covariate networks in classical trigeminal neuralgia patients. Cereb Cortex 2024; 34:bhae337. [PMID: 39123310 DOI: 10.1093/cercor/bhae337] [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: 06/18/2024] [Revised: 07/17/2024] [Accepted: 07/27/2024] [Indexed: 08/12/2024] Open
Abstract
Structural covariance networks and causal effects within can provide critical information on gray matter reorganization and disease-related hierarchical changes. Based on the T1WI data of 43 classical trigeminal neuralgia patients and 45 controls, we constructed morphological similarity networks of cortical thickness, sulcal depth, fractal dimension, and gyrification index. Moreover, causal structural covariance network analyses were conducted in regions with morphological abnormalities or altered nodal properties, respectively. We found that patients showed reduced sulcal depth, gyrification index, and fractal dimension, especially in the salience network and the default mode network. Additionally, the integration of the fractal dimension and sulcal depth networks was significantly reduced, accompanied by decreased nodal efficiency of the bilateral temporal poles, and right pericalcarine cortex within the sulcal depth network. Negative causal effects existed from the left insula to the right caudal anterior cingulate cortex in the gyrification index map, also from bilateral temporal poles to right pericalcarine cortex within the sulcal depth network. Collectively, patients exhibited impaired integrity of the covariance networks in addition to the abnormal gray matter morphology in the salience network and default mode network. Furthermore, the patients may experience progressive impairment in the salience network and from the limbic system to the sensory system in network topology, respectively.
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Affiliation(s)
- Pengfei Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Lane, Wuhou District, Chengdu, Sichuan 610041, China
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Xinyue Wan
- Department of Radiology, Huashan Hospital, Fudan University, No. 12, Urumqi Middle Road, Jingan District, Shanghai 200040, China
| | - Jingqi Jiang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Yang Liu
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Danyang Wang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Kai Ai
- Department of Clinical and Technical Supports, Philips Healthcare, No. 64 West Section, South 2nd Ring Road, Yanta District, Xi'an 710000, China
| | - Guangyao Liu
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Xinding Zhang
- Department of Neurosurgery and Laboratory of Neurosurgery, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
| | - Jing Zhang
- Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730000, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Medical MRI Equipment Application Industry Technology Center, The Second Hospital & Clinical Medical School, Lanzhou University, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
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11
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Kostoglou K, Michmizos KP, Stathis P, Sakas D, Nikita KS, Mitsis GD. Spiking Laguerre Volterra networks-predicting neuronal activity from local field potentials. J Neural Eng 2024; 21:046030. [PMID: 39029490 DOI: 10.1088/1741-2552/ad6594] [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] [Accepted: 07/19/2024] [Indexed: 07/21/2024]
Abstract
Objective.Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations.Approach.Here, we fill this gap by proposing novel spiking Laguerre-Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics.Main results.The proposed networks were applied on extracellular microelectrode recordings of Parkinson's Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features.Significance.These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.
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Affiliation(s)
- Kyriaki Kostoglou
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- Department of Electrical and Computer Engineering, McGill University, Montreal, Canada
| | | | - Pantelis Stathis
- Department of Neurosurgery, National and Kapodistrian University of Athens, Athens, Greece
| | - Damianos Sakas
- Department of Neurosurgery, National and Kapodistrian University of Athens, Athens, Greece
| | - Konstantina S Nikita
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
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12
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Piramide N, De Micco R, Siciliano M, Silvestro M, Tessitore A. Resting-State Functional MRI Approaches to Parkinsonisms and Related Dementia. Curr Neurol Neurosci Rep 2024:10.1007/s11910-024-01365-8. [PMID: 39046642 DOI: 10.1007/s11910-024-01365-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/12/2024] [Indexed: 07/25/2024]
Abstract
PURPOSE OF THE REVIEW In this review, we attempt to summarize the most updated studies that applied resting-state functional magnetic resonance imaging (rs-fMRI) in the field of Parkinsonisms and related dementia. RECENT FINDINGS Over the past decades, increasing interest has emerged on investigating the presence and pathophysiology of cognitive symptoms in Parkinsonisms and their possible role as predictive biomarkers of neurodegenerative brain processes. In recent years, evidence has been provided, applying mainly three methodological approaches (i.e. seed-based, network-based and graph-analysis) on rs-fMRI data, with promising results. Neural correlates of cognitive impairment and dementia have been detected in patients with Parkinsonisms along the diseases course. Interestingly, early functional connectivity signatures were proposed to track and predict future progression of neurodegenerative processes. However, longitudinal studies are still sparce and further investigations are needed to overcome this knowledge gap.
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Affiliation(s)
- Noemi Piramide
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Rosa De Micco
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Mattia Siciliano
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy
- Neuropsychology Laboratory, Department of Psychology, University of Campania "Luigi Vanvitelli", Caserta, Italy
| | - Marcello Silvestro
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Alessandro Tessitore
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Napoli, Italy.
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13
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Lim AMW, Lim EU, Chen PL, Fann CSJ. Unsupervised clustering identified clinically relevant metabolic syndrome endotypes in UK and Taiwan Biobanks. iScience 2024; 27:109815. [PMID: 39040048 PMCID: PMC11260869 DOI: 10.1016/j.isci.2024.109815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 02/02/2024] [Accepted: 04/23/2024] [Indexed: 07/24/2024] Open
Abstract
Metabolic syndrome (MetS) is a collection of cardiovascular risk factors; however, the high prevalence and heterogeneity impede effective clinical management. We conducted unsupervised clustering on individuals from UK Biobank to reveal endotypes. Five MetS subgroups were identified: Cluster 1 (C1): non-descriptive, Cluster 2 (C2): hypertensive, Cluster 3 (C3): obese, Cluster 4 (C4): lipodystrophy-like, and Cluster 5 (C5): hyperglycemic. For all of the endotypes, we identified the corresponding cardiometabolic traits and their associations with clinical outcomes. Genome-wide association studies (GWASs) were conducted to identify associated genotypic traits. We then determined endotype-specific genotypic traits and constructed polygenic risk score (PRS) models specific to each endotype. GWAS of each MetS clusters revealed different genotypic traits. C1 GWAS revealed novel findings of TRIM63, MYBPC3, MYLPF, and RAPSN. Intriguingly, C1, C3, and C4 were associated with genes highly expressed in brain tissues. MetS clusters with comparable phenotypic and genotypic traits were identified in Taiwan Biobank.
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Affiliation(s)
- Aylwin Ming Wee Lim
- Taiwan International Graduate Program in Molecular Medicine, National Yang Ming Chiao Tung University and Academia Sinica, Taipei 112304, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
- ASUS Intelligent Cloud Services (AICS), Taipei 112, Taiwan
| | - Evan Unit Lim
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
- College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Pei-Lung Chen
- Graduate Institute of Medical Genomics and Proteomics, College of Medicine, National Taiwan University, Taipei 10617, Taiwan
- Department of Medical Genetics, National Taiwan University Hospital, Taipei 100, Taiwan
| | - Cathy Shen Jang Fann
- Taiwan International Graduate Program in Molecular Medicine, National Yang Ming Chiao Tung University and Academia Sinica, Taipei 112304, Taiwan
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
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14
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Fan CC, Han C, Wang XM, Chhetri JK, Mao W, Xu EH, Liu SY, Chan P. Data-Driven Subtypes of Multiple System Atrophy and Their Implications for Prognosis. JOURNAL OF PARKINSON'S DISEASE 2024:JPD240040. [PMID: 39031382 DOI: 10.3233/jpd-240040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/22/2024]
Abstract
Background While multiple system atrophy (MSA) presents with high heterogeneous motor and nonmotor symptoms, the associations between clinical phenotypes and prognosis are unclear. Objective We aimed to evaluate clinical phenotypes of MSA using data-driven approach and measure the impact of phenotypes on survival and bedbound status. Methods 193 MSA patients were recruited from Xuanwu Hospital Capital Medical University, whose history, motor and non-motor symptoms were examined using cluster analysis. Ninety-five participants were followed-up via telephone after a mean of 31.87 months. We employed Kaplan- Meier analysis to examine survival and performed Cox and logistic regression analyses to identify factors associated with survival and bedbound status. Results We identified four clinical profiles of MSA: cerebellar symptom-dominant, sleep and mood disorder-dominant, rigid akinetic-dominant, and malignant diffuse. The overall median survival was 7.75 years (95% CI 7.19-8.31). After adjusting for years from symptom onset to diagnosis, age and sex, patients in the malignant diffuse and rigid akinetic-dominant clusters had greater risk of death than sleep and mood disorder-dominant cluster. Furthermore, patients in the malignant diffuse and rigid akinetic-dominant clusters had higher risk of being bedbound than cerebellar symptom-dominant cluster. Conclusions The malignant diffuse and sleep and mood disorder-dominant were identified besides the two classical subtypes, parkinsonism, and cerebellar symptom-variant. Patients with rigid-akinetic motor profiles have a worse prognosis than cerebellar symptom-dominant profiles in general. Diffuse symptoms, especially postural instability, and cognitive alterations at diagnosis, indicate rapid functional loss and disease progression. The different profiles and prognoses might indicate varied underlying pathological mechanisms.
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Affiliation(s)
- Cheng-Cheng Fan
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Chao Han
- National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Xue-Mei Wang
- Department of Neurology, Luhe Hospital, Capital Medical University, Beijing, China
| | - Jagadish K Chhetri
- National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Wei Mao
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Er-He Xu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Shu-Ying Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Chinese Institute for Brain Research (CIBR), Beijing, China
| | - Piu Chan
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Geriatric Diseases, Beijing, China
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15
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Su C, Hou Y, Xu J, Xu Z, Zhou M, Ke A, Li H, Xu J, Brendel M, Maasch JRMA, Bai Z, Zhang H, Zhu Y, Cincotta MC, Shi X, Henchcliffe C, Leverenz JB, Cummings J, Okun MS, Bian J, Cheng F, Wang F. Identification of Parkinson's disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data. NPJ Digit Med 2024; 7:184. [PMID: 38982243 PMCID: PMC11233682 DOI: 10.1038/s41746-024-01175-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Accepted: 06/21/2024] [Indexed: 07/11/2024] Open
Abstract
Parkinson's disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity. This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals' phenotypic progression trajectories for PD subtyping. We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate. We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R. It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R). Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression. In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.
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Grants
- R21 AG083003 NIA NIH HHS
- R01 AG082118 NIA NIH HHS
- R56 AG074001 NIA NIH HHS
- R01AG076448 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1AG072449 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- MJFF-023081 Michael J. Fox Foundation for Parkinson's Research (Michael J. Fox Foundation)
- R01AG080991 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P30 AG072959 NIA NIH HHS
- 3R01AG066707-01S1 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R21AG083003 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG066707 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R35 AG071476 NIA NIH HHS
- RF1 AG082211 NIA NIH HHS
- R56AG074001 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG082118 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R25 AG083721 NIA NIH HHS
- RF1AG082211 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01 NS093334 NINDS NIH HHS
- AG083721-01 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1NS133812 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20GM109025 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1 NS133812 NINDS NIH HHS
- R35AG71476 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01 AG073323 NIA NIH HHS
- R01 AG066707 NIA NIH HHS
- R01AG053798 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01AG076234 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- R01 AG076448 NIA NIH HHS
- R01 AG080991 NIA NIH HHS
- R01 AG076234 NIA NIH HHS
- U01NS093334 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- P20 GM109025 NIGMS NIH HHS
- P30AG072959 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- RF1 AG072449 NIA NIH HHS
- R01 AG053798 NIA NIH HHS
- 3R01AG066707-02S1 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- U01AG073323 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- ALZDISCOVERY-1051936 Alzheimer's Association
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Affiliation(s)
- Chang Su
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Yu Hou
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Jielin Xu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Manqi Zhou
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Alison Ke
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computational Biology, Cornell University, Ithaca, NY, USA
| | - Haoyang Li
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Matthew Brendel
- Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Jacqueline R M A Maasch
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Department of Computer Science, Cornell Tech, Cornell University, New York, NY, USA
| | - Zilong Bai
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA
| | - Haotan Zhang
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Yingying Zhu
- Department of Computer Science, University of Texas at Arlington, Arlington, TX, USA
| | - Molly C Cincotta
- Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA
| | - Claire Henchcliffe
- Department of Neurology, University of California Irvine, Irvine, CA, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Pam Quirk Brain Health and Biomarker Laboratory, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Michael S Okun
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA.
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, Cornell University, New York, NY, USA.
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16
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Wang Q, Gu X, Yang L, Jiang Y, Zhang J, He J. Emerging perspectives on precision therapy for Parkinson's disease: multidimensional evidence leading to a new breakthrough in personalized medicine. Front Aging Neurosci 2024; 16:1417515. [PMID: 39026991 PMCID: PMC11254646 DOI: 10.3389/fnagi.2024.1417515] [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: 04/15/2024] [Accepted: 06/17/2024] [Indexed: 07/20/2024] Open
Abstract
PD is a prevalent and progressive neurodegenerative disorder characterized by both motor and non-motor symptoms. Genes play a significant role in the onset and progression of the disease. While the complexity and pleiotropy of gene expression networks have posed challenges for gene-targeted therapies, numerous pathways of gene variant expression show promise as therapeutic targets in preclinical studies, with some already in clinical trials. With the recognition of the numerous genes and complex pathways that can influence PD, it may be possible to take a novel approach to choose a treatment for the condition. This approach would be based on the symptoms, genomics, and underlying mechanisms of the disease. We discuss the utilization of emerging genetic and pathological knowledge of PD patients to categorize the disease into subgroups. Our long-term objective is to generate new insights for the therapeutic approach to the disease, aiming to delay and treat it more effectively, and ultimately reduce the burden on individuals and society.
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Affiliation(s)
- Qiaoli Wang
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Xuan Gu
- Department of Trauma center, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Le Yang
- Department of Endocrinology, The People’s Hospital of Jilin Province, Changchun, China
| | - Yan Jiang
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Jiao Zhang
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Jinting He
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
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17
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Cash TV, Lessov-Schlaggar CN, Foster ER, Myers PS, Jackson JJ, Maiti B, Kotzbauer PT, Perlmutter JS, Campbell MC. Replication and reliability of Parkinson's disease clinical subtypes. Parkinsonism Relat Disord 2024; 124:107016. [PMID: 38838453 DOI: 10.1016/j.parkreldis.2024.107016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/24/2024] [Accepted: 05/19/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND We recently identified three distinct Parkinson's disease subtypes: "motor only" (predominant motor deficits with intact cognition and psychiatric function); "psychiatric & motor" (prominent psychiatric symptoms and moderate motor deficits); "cognitive & motor" (cognitive and motor deficits). OBJECTIVE We used an independent cohort to replicate and assess reliability of these Parkinson's disease subtypes. METHODS We tested our original subtype classification with an independent cohort (N = 100) of Parkinson's disease participants without dementia and the same comprehensive evaluations assessing motor, cognitive, and psychiatric function. Next, we combined the original (N = 162) and replication (N = 100) datasets to test the classification model with the full combined dataset (N = 262). We also generated 10 random split-half samples of the combined dataset to establish the reliability of the subtype classifications. Latent class analyses were applied to the replication, combined, and split-half samples to determine subtype classification. RESULTS First, LCA supported the three-class solution - Motor Only, Psychiatric & Motor, and Cognitive & Motor- in the replication sample. Next, using the larger, combined sample, LCA again supported the three subtype groups, with the emergence of a potential fourth group defined by more severe motor deficits. Finally, split-half analyses showed that the three-class model also had the best fit in 13/20 (65%) split-half samples; two-class and four-class solutions provided the best model fit in five (25%) and two (10%) split-half replications, respectively. CONCLUSIONS These results support the reproducibility and reliability of the Parkinson's disease behavioral subtypes of motor only, psychiatric & motor, and cognitive & motor groups.
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Affiliation(s)
- Therese V Cash
- Department of Neurology, Washington University School of Medicine, USA
| | | | - Erin R Foster
- Department of Neurology, Washington University School of Medicine, USA; Department of Psychiatry, Washington University School of Medicine, USA; Program in Occupational Therapy, Washington University School of Medicine, USA
| | - Peter S Myers
- Department of Neurology, Washington University School of Medicine, USA
| | - Joshua J Jackson
- Department of Psychological and Brain Sciences, Washington University in St. Louis, USA
| | - Baijayanta Maiti
- Department of Neurology, Washington University School of Medicine, USA; Department of Radiology, Washington University School of Medicine, USA
| | - Paul T Kotzbauer
- Department of Neurology, Washington University School of Medicine, USA
| | - Joel S Perlmutter
- Department of Neurology, Washington University School of Medicine, USA; Department of Radiology, Washington University School of Medicine, USA; Department of Neuroscience, Washington University School of Medicine, USA; Program in Occupational Therapy, Washington University School of Medicine, USA; Program in Physical Therapy, Washington University School of Medicine, USA
| | - Meghan C Campbell
- Department of Neurology, Washington University School of Medicine, USA; Department of Radiology, Washington University School of Medicine, USA.
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18
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Fabbri M, Campisi C, Ledda C, Rinaldi D, Tsukita K, Romagnolo A, Imbalzano G, Zibetti M, Rizzone MG, Pontieri FE, Lopiano L, Artusi CA. Incidence and predictors of postural abnormalities in Parkinson's disease: a PPMI cohort study. J Neurol 2024; 271:4628-4634. [PMID: 38796527 DOI: 10.1007/s00415-024-12457-3] [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: 03/03/2024] [Revised: 05/06/2024] [Accepted: 05/19/2024] [Indexed: 05/28/2024]
Abstract
BACKGROUND Axial postural abnormalities (PA) are invalidating symptoms of Parkinson's disease (PD). Risk factors for PA are unknown. OBJECTIVES We sought to evaluate PA incidence and risk factors over the first 4-6 years of PD. METHODS We included 441 PD patients from the Parkinson's Progression Markers Initiative (PPMI) cohort with data at diagnosis and after 4-year follow-up. PA was defined according to a posture item ≥ 2 at the Movement Disorder Society-sponsored-revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) in Off therapeutic condition. The Kruskal-Wallis test was used to compare characteristics of patients without PA ('no-PA'), with PA at disease onset ('baseline-PA'), and PA developed during follow-up ('develop-PA'). To identify predictors of PA development, univariate and multivariate Cox regression analyses were performed considering demographic, clinical and therapeutic variables. RESULTS 10.9% of patients showed PA at baseline and 23.7% developed PA within the first 4-6 years since diagnosis. Older age, malignant phenotype, higher MDS-UPDRS part III, Hoehn & Yahr, and dysautonomia (SCOPA-AUT) score, and lower levels of physical activity were predictors of PA development at the univariate analysis. Older age (Hazard ratio [HR] per year: 1.041) and higher MDS-UPDRS part III score (HR per point: 1.035) survived as PA development predictors in the multivariate analysis. CONCLUSIONS PPMI cohort data show that > 30% of PD patients present PA within the first 4-6 years of disease. Older age at onset and higher motor burden are associated with a higher risk for PA development. The protective role of physical activity merits to be further investigated.
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Affiliation(s)
- Margherita Fabbri
- Department of Neurosciences, Toulouse Parkinson Expert Center, Centre d'Investigation Clinique de Toulouse CIC1436, NS-Park/FCRIN Network, University Hospital of Toulouse, INSERM, University of Toulouse 3, Toulouse, France
| | - Corrado Campisi
- Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
| | - Claudia Ledda
- Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
- SC Neurologia 2U, AOU Città Della Salute E Della Scienza, Corso Bramante 88, 10126, Turin, Italy
| | - Domiziana Rinaldi
- Dipartimento Di Neuroscienze, Salute Mentale E Organi Di Senso, Sapienza Università Di Roma, Via Di Grottarossa, 1035-00189, Rome, Italy
| | - Kazuto Tsukita
- Department of Neurology, Graduate School of Medicine, Kyoto University, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Alberto Romagnolo
- Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
- SC Neurologia 2U, AOU Città Della Salute E Della Scienza, Corso Bramante 88, 10126, Turin, Italy
| | - Gabriele Imbalzano
- Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
- SC Neurologia 2U, AOU Città Della Salute E Della Scienza, Corso Bramante 88, 10126, Turin, Italy
| | - Maurizio Zibetti
- Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
- SC Neurologia 2U, AOU Città Della Salute E Della Scienza, Corso Bramante 88, 10126, Turin, Italy
| | - Mario Giorgio Rizzone
- Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
- SC Neurologia 2U, AOU Città Della Salute E Della Scienza, Corso Bramante 88, 10126, Turin, Italy
| | - Francesco Ernesto Pontieri
- Dipartimento Di Neuroscienze, Salute Mentale E Organi Di Senso, Sapienza Università Di Roma, Via Di Grottarossa, 1035-00189, Rome, Italy
| | - Leonardo Lopiano
- Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy
- SC Neurologia 2U, AOU Città Della Salute E Della Scienza, Corso Bramante 88, 10126, Turin, Italy
| | - Carlo Alberto Artusi
- Department of Neuroscience "Rita Levi Montalcini", University of Turin, Turin, Italy.
- SC Neurologia 2U, AOU Città Della Salute E Della Scienza, Corso Bramante 88, 10126, Turin, Italy.
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19
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Barbosa RMG, Soares MC, Portela DMMC, Guimarães TG, Cury RG. New Perspectives of Deep Brain Stimulation Indications for Parkinson's Disease: A Critical Review. Brain Sci 2024; 14:638. [PMID: 39061379 PMCID: PMC11274985 DOI: 10.3390/brainsci14070638] [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: 06/04/2024] [Revised: 06/23/2024] [Accepted: 06/24/2024] [Indexed: 07/28/2024] Open
Abstract
Deep Brain Stimulation (DBS) is an effective treatment option for patients with dopaminergic complications of Parkinson's disease (PD) and drug-refractory PD tremor. However, DBS and its indications can be challenging, and they are not often debated in the medical community. Through a critical narrative review, the objective of this paper is to improve the comprehension of DBS indications and help to solve the puzzle that this process can be. Proper patient selection is the first step for a good surgical outcome. In this review, then, relevant considerations are discussed, involving PD genes, PD phenotypes, indications of early stages, non-motor symptoms, neuroimaging predictors, comorbidities, and age. Individualized approaches are encouraged, including clinical and radiological factors. Social support during the whole follow-up and expectations alignment are necessary through this process and are also debated.
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Affiliation(s)
- Renata Montes Garcia Barbosa
- Movement Disorders Center, Department of Neurology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil; (R.M.G.B.); (M.C.S.); (T.G.G.)
| | - Miriam Carvalho Soares
- Movement Disorders Center, Department of Neurology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil; (R.M.G.B.); (M.C.S.); (T.G.G.)
| | - Denise Maria Meneses Cury Portela
- Movement Disorders Center, Department of Neurology, School of Medicine, Centro Universitário Uninovafapi (UNINOVAFAPI), Teresina 64073505, Brazil;
| | - Thiago Gonçalves Guimarães
- Movement Disorders Center, Department of Neurology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil; (R.M.G.B.); (M.C.S.); (T.G.G.)
| | - Rubens Gisbert Cury
- Movement Disorders Center, Department of Neurology, School of Medicine, University of São Paulo, São Paulo 05403-010, Brazil; (R.M.G.B.); (M.C.S.); (T.G.G.)
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20
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Fabbri M, Rascol O, Foltynie T, Carroll C, Postuma RB, Porcher R, Corvol JC. Advantages and Challenges of Platform Trials for Disease Modifying Therapies in Parkinson's Disease. Mov Disord 2024. [PMID: 38925541 DOI: 10.1002/mds.29899] [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: 04/19/2024] [Revised: 05/27/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Traditional drug development in Parkinson's disease (PD) faces significant challenges because of its protracted timeline and high costs. In response, innovative master protocols are emerging and designed to address multiple research questions within a single overarching protocol. These trials may offer advantages such as increased efficiency, agility in adding new treatment arms, and potential cost savings. However, they also present organizational, methodological, funding, regulatory, and sponsorship challenges. We review the potential of master protocols, focusing on platform trials, for disease modifying therapies in PD. These trials share a common control group and allow for the termination or addition of treatment arms during a trial with non-predetermined end. Specific issues exist for a platform trial in the PD field considering the heterogeneity of patients in terms of phenotype, genotype and staging, the confounding effects of symptomatic treatments, and the choice of outcome measures with no consensus on a non-clinical biomarker to serve as a surrogate and the slowness of PD progression. We illustrate these aspects using the examples of the main PD platform trials currently in development with each one targeting distinct goals, populations, and outcomes. Overall, platform trials hold promise in expediting the evaluation of potential therapies for PD. However, it remains to be proven whether these theoretical benefits will translate into increased production of high-quality trial data. Success also depends on the willingness of pharmaceutical companies to engage in such trials and whether this approach will ultimately hasten the identification and licensing of effective disease-modifying drugs. © 2024 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Margherita Fabbri
- Department of Clinical Pharmacology and Neurosciences, Clinical Investigation Center CIC1436, Toulouse Parkinson Expert Center, Toulouse NeuroToul Center of Excellence in Neurodegeneration (COEN), French NS-Park/F-CRIN Network, University of Toulouse 3, CHU of Toulouse, INSERM, Toulouse, France
| | - Olivier Rascol
- Department of Clinical Pharmacology and Neurosciences, Clinical Investigation Center CIC1436, Toulouse Parkinson Expert Center, Toulouse NeuroToul Center of Excellence in Neurodegeneration (COEN), French NS-Park/F-CRIN Network, University of Toulouse 3, CHU of Toulouse, INSERM, Toulouse, France
| | - Tom Foltynie
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, London, United Kingdom
| | - Camille Carroll
- Translational and Clinical Research Institute, Newcastle University, Newcastle, United Kingdom
| | - Ronald B Postuma
- Department of Neurology and Neurosurgery, McGill University, Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Raphael Porcher
- Université Paris Cité and Université Sorbonne Paris Nord, INSERM, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Paris, France
- Center for Clinical Epidemiology, Assistance Publique-Hôpitaux de Paris, Hôtel-Dieu Hospital, Paris, France
| | - Jean Christophe Corvol
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute - ICM, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Department of Neurology, CIC Neurosciences, Hôpital Pitié-Salpêtrière, French NS-Park/F-CRIN Network, Paris, France
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21
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Pietracupa S, Ojha A, Belvisi D, Piervincenzi C, Tommasin S, Petsas N, De Bartolo MI, Costanzo M, Fabbrini A, Conte A, Berardelli A, Pantano P. Understanding the role of cerebellum in early Parkinson's disease: a structural and functional MRI study. NPJ Parkinsons Dis 2024; 10:119. [PMID: 38898032 PMCID: PMC11187155 DOI: 10.1038/s41531-024-00727-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
Increasing evidence suggests that the cerebellum may have a role in the pathophysiology of Parkinson's disease (PD). Hence, the scope of this study was to investigate whether there are structural and functional alterations of the cerebellum and whether they correlate with motor and non-motor symptoms in early PD patients. Seventy-six patients with early PD and thirty-one age and sex-matched healthy subjects (HS) were enrolled and underwent a 3 T magnetic resonance imaging (MRI) protocol. The following MRI analyses were performed: (1) volumes of 5 cerebellar regions of interest (sensorimotor and cognitive cerebellum, dentate, interposed, and fastigial nuclei); (2) microstructural integrity of the cerebellar white matter connections (inferior, middle, and superior cerebellar peduncles); (3) functional connectivity at rest of the 5 regions of interest already described in point 1 with the rest of brain. Compared to controls, early PD patients showed a significant decrease in gray matter volume of the dentate, interposed and fastigial nuclei, bilaterally. They also showed abnormal, bilateral white matter microstructural integrity in all 3 cerebellar peduncles. Functional connectivity of the 5 cerebellar regions of interest with several areas in the midbrain, basal ganglia and cerebral cortex was altered. Finally, there was a positive correlation between abnormal functional connectivity of the fastigial nucleus with the volume of the nucleus itself and a negative correlation with axial symptoms severity. Our results showed that structural and functional alterations of the cerebellum are present in PD patients and these changes contribute to the pathophysiology of PD in the early phase.
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Affiliation(s)
- S Pietracupa
- IRCCS Neuromed, Pozzilli, IS, Italy
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - A Ojha
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - D Belvisi
- IRCCS Neuromed, Pozzilli, IS, Italy
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - C Piervincenzi
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy.
| | - S Tommasin
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - N Petsas
- Department of Public Health and Infectious Disease, Sapienza University of Rome, Rome, Italy
| | | | | | - A Fabbrini
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - A Conte
- IRCCS Neuromed, Pozzilli, IS, Italy
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - A Berardelli
- IRCCS Neuromed, Pozzilli, IS, Italy
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - P Pantano
- IRCCS Neuromed, Pozzilli, IS, Italy
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
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22
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Chen Z, He C, Zhang P, Cai X, Li X, Huang W, Huang S, Cai M, Wang L, Zhan P, Zhang Y. Brain network centrality and connectivity are associated with clinical subtypes and disease progression in Parkinson's disease. Brain Imaging Behav 2024; 18:646-661. [PMID: 38337128 DOI: 10.1007/s11682-024-00862-1] [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] [Accepted: 01/30/2024] [Indexed: 02/12/2024]
Abstract
To investigate brain network centrality and connectivity alterations in different Parkinson's disease (PD) clinical subtypes using resting-state functional magnetic resonance imaging (RS-fMRI), and to explore the correlation between baseline connectivity changes and the clinical progression. Ninety-two PD patients were enrolled at baseline, alongside 38 age- and sex-matched healthy controls. Of these, 85 PD patients underwent longitudinal assessments with a mean of 2.75 ± 0.59 years. Two-step cluster analysis integrating comprehensive motor and non-motor manifestations was performed to define PD subtypes. Degree centrality (DC) and secondary seed-based functional connectivity (FC) were applied to identify brain network centrality and connectivity changes among groups. Regression analysis was used to explore the correlation between baseline connectivity changes and clinical progression. Cluster analysis identified two main PD subtypes: mild PD and moderate PD. Two different subtypes within the mild PD were further identified: mild motor-predominant PD and mild-diffuse PD. Accordingly, the disrupted DC and seed-based FC in the left inferior frontal orbital gyrus and left superior occipital gyrus were severe in moderate PD. The DC and seed-based FC alterations in the right gyrus rectus and right postcentral gyrus were more severe in mild-diffuse PD than in mild motor-predominant PD. Moreover, disrupted DC were associated with clinical manifestations at baseline in patients with PD and predicted motor aspects progression over time. Our study suggested that brain network centrality and connectivity changes were different among PD subtypes. RS-fMRI holds promise to provide an objective assessment of subtype-related connectivity changes and predict disease progression in PD.
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Affiliation(s)
- Zhenzhen Chen
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
- Department of Neurology, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, 430014, China
- Guangzhou Key Laboratory of Diagnosis and Treatment for Neurodegenerative Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Chentao He
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
- Guangzhou Key Laboratory of Diagnosis and Treatment for Neurodegenerative Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Piao Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Xin Cai
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Xiaohong Li
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Wenlin Huang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Sifei Huang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Mengfei Cai
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Lijuan Wang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China
| | - Peiyan Zhan
- Department of Neurology, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and Technology, Wuhan, 430014, China
| | - Yuhu Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106 Zhongshan Er Road, Guangzhou, Guangdong Province, 510080, China.
- Guangzhou Key Laboratory of Diagnosis and Treatment for Neurodegenerative Diseases, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
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23
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Li X, Luo M, Xu H, Jia L, Liang Y, Xu Q, Wang Y. CAP2 contributes to Parkinson's disease diagnosed by neutrophil extracellular trap-related immune activity. Front Immunol 2024; 15:1377409. [PMID: 38846945 PMCID: PMC11153744 DOI: 10.3389/fimmu.2024.1377409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 04/29/2024] [Indexed: 06/09/2024] Open
Abstract
Introduction Neutrophil extracellular traps (NETs) constitute a crucial element of the immune system, and dysfunction in immune responses is implicated in the susceptibility and progression of Parkinson's disease (PD). Nevertheless, the mechanism connecting PD and NETs remains unclear. This study aims to uncover potential NETs-related immune biomarkers and elucidate their role in PD pathogenesis. Methods Through differential gene analysis of PD and NETs in GSE7621 datasets, we identified two PD subtypes and explored potential biological pathways. Subsequently, using ClusterWGCNA, we pinpointed pertinent genes and developed clinical diagnostic models. We then optimized the chosen model and evaluated its association with immune infiltration. Validation was conducted using the GSE20163 dataset. Screening the single-cell dataset GSE132758 revealed cell populations associated with the identified gene. Results Our findings identified XGB as the optimal diagnostic model, with CAP2 identified as a pivotal gene. The risk model effectively predicted overall diagnosis rates, demonstrating a robust correlation between infiltrating immune cells and genes related to the XGB model. Discussion In conclusions, we identified PD subtypes and diagnostic genes associated with NETs, highlighting CAP2 as a pivotal gene. These findings have significant implications for understanding potential molecular mechanisms and treatments for PD.
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Affiliation(s)
| | | | | | | | | | | | - Yonghui Wang
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, China
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24
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Hähnel T, Raschka T, Sapienza S, Klucken J, Glaab E, Corvol JC, Falkenburger BH, Fröhlich H. Progression subtypes in Parkinson's disease identified by a data-driven multi cohort analysis. NPJ Parkinsons Dis 2024; 10:95. [PMID: 38698004 PMCID: PMC11066039 DOI: 10.1038/s41531-024-00712-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 04/16/2024] [Indexed: 05/05/2024] Open
Abstract
The progression of Parkinson's disease (PD) is heterogeneous across patients, affecting counseling and inflating the number of patients needed to test potential neuroprotective treatments. Moreover, disease subtypes might require different therapies. This work uses a data-driven approach to investigate how observed heterogeneity in PD can be explained by the existence of distinct PD progression subtypes. To derive stable PD progression subtypes in an unbiased manner, we analyzed multimodal longitudinal data from three large PD cohorts and performed extensive cross-cohort validation. A latent time joint mixed-effects model (LTJMM) was used to align patients on a common disease timescale. Progression subtypes were identified by variational deep embedding with recurrence (VaDER). In each cohort, we identified a fast-progressing and a slow-progressing subtype, reflected by different patterns of motor and non-motor symptoms progression, survival rates, treatment response, features extracted from DaTSCAN imaging and digital gait assessments, education, and Alzheimer's disease pathology. Progression subtypes could be predicted with ROC-AUC up to 0.79 for individual patients when a one-year observation period was used for model training. Simulations demonstrated that enriching clinical trials with fast-progressing patients based on these predictions can reduce the required cohort size by 43%. Our results show that heterogeneity in PD can be explained by two distinct subtypes of PD progression that are stable across cohorts. These subtypes align with the brain-first vs. body-first concept, which potentially provides a biological explanation for subtype differences. Our predictive models will enable clinical trials with significantly lower sample sizes by enriching fast-progressing patients.
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Affiliation(s)
- Tom Hähnel
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.
- Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
| | - Tamara Raschka
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany
| | - Stefano Sapienza
- Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Jochen Klucken
- Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Strassen, Luxembourg
| | - Enrico Glaab
- Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Jean-Christophe Corvol
- Sorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of Neurology, Paris, France
| | - Björn H Falkenburger
- Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany
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Ratajska AM, Etheridge CB, Lopez FV, Kenney LE, Rodriguez K, Schade RN, Gertler J, Bowers D. The Relationship Between Autonomic Dysfunction and Mood Symptoms in De Novo Parkinson's Disease Patients Over Time. J Geriatr Psychiatry Neurol 2024; 37:242-252. [PMID: 37831611 PMCID: PMC10990848 DOI: 10.1177/08919887231204542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/15/2023]
Abstract
BACKGROUND Autonomic dysfunction is prevalent in Parkinson's disease (PD) and can worsen quality of life. We examined: (a) whether specific autonomic symptoms were more strongly associated with anxiety or depression in PD and (b) whether overall autonomic dysfunction predicted mood trajectories over a 5-year period. METHODS Newly diagnosed individuals with PD (N = 414) from the Parkinson's Progression Markers Initiative completed self-report measures of depression, anxiety, and autonomic symptoms annually. Cross-sectional linear regressions examined relationships between specific autonomic subdomains (gastrointestinal, cardiovascular, thermoregulatory, etc.) and mood. Multilevel modeling examined longitudinal relationships with total autonomic load. RESULTS Gastrointestinal symptoms were associated with both higher anxiety (b = 1.04, 95% CI [.55, 1.53], P < .001) and depression (b = .24, 95% CI [.11, .37], P = .012), as were thermoregulatory symptoms (anxiety: b = 1.06, 95% CI [.46, 1.65], P = .004; depression: b = .25, 95% CI [.09, .42], P = .013), while cardiovascular (b = .36, 95% CI [.10, .62], P = .012) and urinary symptoms (b = .10, 95% CI [.01, .20], P = .037) were associated only with depression. Longitudinally, higher total autonomic load was associated with increases in both depression (b = .01, 95% CI [.00, .02], P = .015) and anxiety (b = .04, 95% CI [.01, .06], P < .001) over time, as well as occasion-to-occasion fluctuations (depression: b = .08, 95% CI [.05, .10], P < .001; anxiety: b = .24, 95% CI [.15, .32], P < .001). CONCLUSION Findings suggest autonomic dysfunction, particularly gastrointestinal and thermoregulatory symptoms, may be an indicator for elevated anxiety/depression and a potential treatment target early on in PD.
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Affiliation(s)
- Adrianna M. Ratajska
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Connor B. Etheridge
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
| | - Francesca V. Lopez
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Lauren E. Kenney
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Katie Rodriguez
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Rachel N. Schade
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Joshua Gertler
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
| | - Dawn Bowers
- Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
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Liu RH, Xiao XY, Yao L, Jia YY, Guo J, Wang XC, Kong Y, Kong QX. Eukaryotic translation initiation factor EIF4G1 p.Ser637Cys mutation in a family with Parkinson's disease with antecedent essential tremor. Exp Ther Med 2024; 27:206. [PMID: 38590578 PMCID: PMC11000071 DOI: 10.3892/etm.2024.12494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 02/09/2024] [Indexed: 04/10/2024] Open
Abstract
Essential tremor (ET) and Parkinson's disease (PD) are common chronic movement disorders that can cause a substantial degree of disability. However, the etiology underlying these two conditions remains poorly understood. In the present study, Whole-exome sequencing of peripheral blood samples from the proband and Sanger sequencing of the other 18 family members, and pedigree analysis of four generations of 29 individuals with both ET and PD in a nonconsanguineous Chinese family were performed. Specifically, family members who had available medical information, including historical documentation and physical examination records, were included. A novel c.1909A>T (p.Ser637Cys) missense mutation was identified in the eukaryotic translation initiation factor 4γ1 (EIF4G1) gene as the candidate likely responsible for both conditions. In total, 9 family members exhibited tremor of the bilateral upper limbs and/or head starting from ages of ≥40 years, 3 of whom began showing evidence of PD in their 70s. Eukaryotic initiation factor 4 (eIF4)G1, a component of the translation initiation complex eIF4F, serves as a scaffold protein that interacts with many initiation factors and then binds to the 40S ribosomal subunit. The EIF4G1 (p.Ser637Cys) might inhibit the recruitment of the mRNA to the ribosome. In conclusion, the results from the present study suggested that EIF4G1 may be responsible for the hereditary PD with 'antecedent ET' reported in the family assessed.
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Affiliation(s)
- Rui-Han Liu
- Department of Pediatrics, Affiliated Hospital of Jining Medical University, Jining, Shandong 272000, P.R. China
- College of TCM, Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250399, P.R. China
| | - Xiang-Yu Xiao
- Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, P.R. China
| | - Lei Yao
- Clinical Medical College, Jining Medical University, Jining, Shandong 272000, P.R. China
| | - Yuan-Yuan Jia
- Department of Neurology, Affiliated Hospital of Jining Medical University, Jining, Shandong 272000, P.R. China
| | - Jia Guo
- Clinical Medical College, Jining Medical University, Jining, Shandong 272000, P.R. China
| | - Xing-Chen Wang
- Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, P.R. China
| | - Yu Kong
- Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining, Shandong 272000, P.R. China
- College of Materials Science and Engineering, Qingdao University, Qingdao, Shandong 266071, P.R. China
| | - Qing-Xia Kong
- Department of Neurology, Affiliated Hospital of Jining Medical University, Jining, Shandong 272000, P.R. China
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Wang S, Jiang S, Wu J, Miao Y, Duan Y, Mu Z, Wang J, Tang Y, Su M, Guo Z, Yu X, Zhao Y. Trends in parkinson's disease mortality in China from 2004 to 2021: a joinpoint analysis. BMC Public Health 2024; 24:1091. [PMID: 38641581 PMCID: PMC11031848 DOI: 10.1186/s12889-024-18532-8] [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: 09/13/2023] [Accepted: 04/05/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND This study aimed to analyze the trends of Parkinson's disease (PD) mortality rates among Chinese residents from 2004 to 2021, provide evidence for the formulation of PD prevention and control strategies to improve the quality of life among PD residents. METHODS Demographic and sociological data such as gender, urban or rural residency and age were obtained from the National Cause of Death Surveillance Dataset from 2004 to 2021. We then analyzed the trends of PD mortality rates by Joinpoint regression. RESULTS The PD mortality and standardized mortality rates in China showed an overall increasing trend during 2004-2021 (average annual percentage change [AAPC] = 7.14%, AAPCASMR=3.21%, P < 0.001). The mortality and standardized mortality rate in male (AAPC = 7.65%, AAPCASMR=3.18%, P < 0.001) were higher than that of female (AAPC = 7.03%, AAPCASMR=3.09%, P < 0.001). The PD standardized mortality rates of urban (AAPC = 5.13%, AAPCASMR=1.76%, P < 0.001) and rural (AAPC = 8.40%, AAPCASMR=4.29%, P < 0.001) residents both increased gradually. In the age analysis, the mortality rate increased with age. And the mortality rates of those aged > 85 years was the highest. Considering gender, female aged > 85 years had the fastest mortality trend (annual percentage change [APC] = 5.69%, P < 0.001). Considering urban/rural, rural aged 80-84 years had the fastest mortality trend (APC = 6.68%, P < 0.001). CONCLUSIONS The mortality rate of PD among Chinese residents increased from 2004 to 2021. Male sex, urban residence and age > 85 years were risk factors for PD-related death and should be the primary focus for PD prevention.
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Affiliation(s)
- Suxian Wang
- School of Public Health, Zhengzhou University, 450001, Zhengzhou, Henan Province, China
| | - Shuai Jiang
- The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan Province, China
- Institute for Hospital Management of Henan Province, 450052, Zhengzhou, Henan Province, China
| | - Jian Wu
- School of Public Health, Zhengzhou University, 450001, Zhengzhou, Henan Province, China
| | - Yudong Miao
- School of Public Health, Zhengzhou University, 450001, Zhengzhou, Henan Province, China
| | - Yanran Duan
- The First Affiliated Hospital of Zhengzhou University, 450052, Zhengzhou, Henan Province, China
| | - Zihan Mu
- School of Public Health, Zhengzhou University, 450001, Zhengzhou, Henan Province, China
| | - Jing Wang
- School of Public Health, Zhengzhou University, 450001, Zhengzhou, Henan Province, China
| | - Yanyu Tang
- School of Public Health, Zhengzhou University, 450001, Zhengzhou, Henan Province, China
| | - Mingzhu Su
- School of Public Health, Zhengzhou University, 450001, Zhengzhou, Henan Province, China
| | - Zixu Guo
- School of Public Health, Zhengzhou University, 450001, Zhengzhou, Henan Province, China
| | - Xueqing Yu
- School of Public Health, Zhengzhou University, 450001, Zhengzhou, Henan Province, China
| | - Yaojun Zhao
- School of Public Health, Zhengzhou University, 450001, Zhengzhou, Henan Province, China.
- Central China Fuwai Hospital, Central China Fuwai Hospital of Zhengzhou University, 451460, Zhengzhou, Henan Province, China.
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Novakova L, Gajdos M, Barton M, Brabenec L, Zeleznikova Z, Moravkova I, Rektorova I. Striato-cortical functional connectivity changes in mild cognitive impairment with Lewy bodies. Parkinsonism Relat Disord 2024; 121:106031. [PMID: 38364623 DOI: 10.1016/j.parkreldis.2024.106031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/01/2024] [Accepted: 02/09/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Functional connectivity changes in clinically overt neurodegenerative diseases such as dementia with Lewy bodies have been described, but studies on connectivity changes in the pre-dementia phase are scarce. OBJECTIVES We concentrated on evaluating striato-cortical functional connectivity differences between patients with Mild Cognitive Impairment with Lewy bodies and healthy controls and on assessing the relation to cognition. METHODS Altogether, we enrolled 77 participants (47 patients, of which 35 met all the inclusion criteria for the final analysis, and 30 age- and gender-matched healthy controls, of which 28 met all the inclusion criteria for the final analysis) to study the seed-based connectivity of the dorsal, middle, and ventral striatum. We assessed correlations between functional connectivity in the regions of between-group differences and neuropsychological scores of interest (visuospatial and executive domains z-scores). RESULTS Subjects with Mild Cognitive Impairment with Lewy Bodies, as compared to healthy controls, showed increased connectivity from the dorsal part of the striatum particularly to the bilateral anterior part of the temporal cortex with an association with executive functions. CONCLUSIONS We were able to capture early abnormal connectivity within cholinergic and noradrenergic pathways that correlated with cognitive functions known to be linked to cholinergic/noradrenergic deficits. The knowledge of specific alterations may improve our understanding of early neural changes in pre-dementia stages and enhance research of disease modifying therapy.
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Affiliation(s)
- Lubomira Novakova
- Brain and Mind Research Program, CEITEC, Masaryk University, Brno, Czech Republic
| | - Martin Gajdos
- Brain and Mind Research Program, CEITEC, Masaryk University, Brno, Czech Republic
| | - Marek Barton
- Brain and Mind Research Program, CEITEC, Masaryk University, Brno, Czech Republic
| | - Lubos Brabenec
- Brain and Mind Research Program, CEITEC, Masaryk University, Brno, Czech Republic
| | - Zaneta Zeleznikova
- Brain and Mind Research Program, CEITEC, Masaryk University, Brno, Czech Republic; First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Ivona Moravkova
- Brain and Mind Research Program, CEITEC, Masaryk University, Brno, Czech Republic; First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Irena Rektorova
- Brain and Mind Research Program, CEITEC, Masaryk University, Brno, Czech Republic; First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic.
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29
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Ou R, Liu K, Lin J, Yang T, Xiao Y, Wei Q, Hou Y, Li C, Zhang L, Jiang Z, Zhao B, Chen X, Song W, Wu Y, Shang H. Relationship between plasma NFL and disease progression in Parkinson's disease: a prospective cohort study. J Neurol 2024; 271:1837-1843. [PMID: 38063869 DOI: 10.1007/s00415-023-12117-y] [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: 09/28/2023] [Revised: 11/14/2023] [Accepted: 11/14/2023] [Indexed: 03/28/2024]
Abstract
OBJECTIVE We aimed to examine the longitudinal change of plasma neurofilament light chain (NFL) level and explore its diagnostic and prognostic implications in Parkinson's disease (PD). METHODS A total of 184 patients with early PD who completed 5-year annually repeated clinical assessments were included. Plasma NFL at baseline, 1 year, and 2 year were examined, which were quantified using the ultrasensitive Simoa technology. At baseline, blood from 86 sex- and age-matched healthy controls (HC) were obtained for comparison. RESULTS Plasma NFL in PD patients at baseline was significantly higher than those in HC (P = 0.046), and significantly increased after 2 years (P = 0.046). Receiver operating characteristic curve indicated that a plasma NFL cut-off value of 10.79 pg/mL resulted in 39.7% sensitivity and 84.0% specificity, with an area under the curve of 0.635, to distinguish PD from HC (P < 0.001). Linear mixed-effect models indicated that baseline plasma NFL (> 9.24 pg/mL) correlated with a greater increase in the Unified Parkinson's Disease Rating Scale III (estimate = 0.651, P = 0.001) and Hoehn & Yahr stage (estimate = 0.072, P < 0.001), and also correlated with a greater decrease in the Montreal Cognitive Assessment (estimate = - 0.387, P < 0.001) during follow-up visits. CONCLUSIONS Plasma NFL exhibits a tendency to increase with disease progression, and elevated baseline plasma NFL can serve as a predictor for accelerated motor deterioration and cognitive decline in PD. However, plasma NFL does not have high accuracy to distinguish individuals with early-stage PD from HC.
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Affiliation(s)
- Ruwei Ou
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Kuncheng Liu
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Junyu Lin
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Tianmi Yang
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Yi Xiao
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Qianqian Wei
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Yanbing Hou
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Chunyu Li
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Lingyu Zhang
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Zheng Jiang
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Bi Zhao
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Xueping Chen
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Wei Song
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Ying Wu
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China
| | - Huifang Shang
- Laboratory of Neurodegenerative Disorders, Department of Neurology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, No.37, Guoxue Lane, Chengdu, 610041, Sichuan, China.
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30
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Orso B, Brosse S, Frasnelli J, Arnaldi D. Opportunities and Pitfalls of REM Sleep Behavior Disorder and Olfactory Dysfunction as Early Markers in Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2024:JPD230348. [PMID: 38517805 DOI: 10.3233/jpd-230348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
Abstract
During its pre-motor stage, Parkinson's disease (PD) presents itself with a multitude of non-motor symptoms with different degrees of specificity and sensitivity. The most important among them are REM sleep behavior disorder (RBD) and olfactory dysfunction. RBD is a parasomnia characterized by the loss of REM sleep muscle atonia and dream-enacting behaviors. Olfactory dysfunction in individuals with prodromal PD is usually described as hyposmia (reduced sense of smell) or anosmia (complete loss of olfactory function). These symptoms can precede the full expression of motor symptoms by decades. A close comprehension of these symptoms and the underlying mechanisms may enable early screening as well as interventions to improve patients' quality of life. Therefore, these symptoms have unmatched potential for identifying PD patients in prodromal stages, not only allowing early diagnosis but potentially opening a window for early, possibly disease-modifying intervention. However, they come with certain challenges. This review addresses some of the key opportunities and pitfalls of both RBD and olfactory dysfunction as early markers of PD.
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Affiliation(s)
- Beatrice Orso
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), Clinical Neurology, University of Genoa, Genoa, Italy
| | - Sarah Brosse
- Department of Anatomy, Université du Québec à Trois-Rivières, Trois-Rivières, Québec, Canada
- Research Center, Sacré-Coeur Hospital of Montreal, Montréal, Québec, Canada
| | - Johannes Frasnelli
- Department of Anatomy, Université du Québec à Trois-Rivières, Trois-Rivières, Québec, Canada
- Research Center, Sacré-Coeur Hospital of Montreal, Montréal, Québec, Canada
| | - Dario Arnaldi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), Clinical Neurology, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico S. Martino, Genoa, Italy
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31
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Malaguti MC, Gios L, Giometto B, Longo C, Riello M, Ottaviani D, Pellegrini M, Di Giacopo R, Donner D, Rozzanigo U, Chierici M, Moroni M, Jurman G, Bincoletto G, Pardini M, Bacchin R, Nobili F, Di Biasio F, Avanzino L, Marchese R, Mandich P, Garbarino S, Pagano M, Campi C, Piana M, Marenco M, Uccelli A, Osmani V. Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study. PLoS One 2024; 19:e0300127. [PMID: 38483951 PMCID: PMC10939244 DOI: 10.1371/journal.pone.0300127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 02/15/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD. OBJECTIVE The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis. METHODS The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers. RESULTS The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy). CONCLUSIONS The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models.
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Affiliation(s)
| | - Lorenzo Gios
- TrentinoSalute4.0 –Competence Center for Digital Health of the Province of Trento, Trento, Italy
| | - Bruno Giometto
- Centro Interdipartimentale di Scienze Mediche (CISMed), Facoltà di Medicina e Chirurgia, Università di Trento, Trento, Italy
| | - Chiara Longo
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
| | - Marianna Riello
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
| | | | | | | | - Davide Donner
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
- Department of Medical and Surgical Sciences, Alma Mater Studiorum Università di Bologna, Bologna, Italy
| | - Umberto Rozzanigo
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
| | | | - Monica Moroni
- Fondazione Bruno Kessler Research Center, Trento, Italy
| | | | | | - Matteo Pardini
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Neuroscience, Rehabilitation, Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Ruggero Bacchin
- Azienda Provinciale per i Servizi Sanitari (APSS) di Trento, Trento, Italy
| | - Flavio Nobili
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Laura Avanzino
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | | | - Paola Mandich
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- DINOGMI Department, University of Genoa, Genoa, Italy
| | | | - Mattia Pagano
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Campi
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Dipartimento Di Matematica, Università Di Genova, Genoa, Italy
| | - Michele Piana
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Dipartimento Di Matematica, Università Di Genova, Genoa, Italy
| | | | | | - Venet Osmani
- Fondazione Bruno Kessler Research Center, Trento, Italy
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Tian J, Zuo C, Shi J, Ma D, Shi C. Peripheral immune cell traits and Parkinson's disease: A Mendelian randomization study. PLoS One 2024; 19:e0299026. [PMID: 38442099 PMCID: PMC10914262 DOI: 10.1371/journal.pone.0299026] [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: 08/14/2023] [Accepted: 02/04/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND The peripheral immune system is altered in Parkinson's disease (PD), but the causal relationship between the two remains controversial. In this study, we aimed to estimate the causal relationship between peripheral immune features and PD using a two-sample Mendelian randomization (MR) approach. METHODS Genome-wide association study (GWAS) data of peripheral blood immune signatures from European populations were used for exposure and PD summary statistics were used as results. We conducted a two-sample MR study using the inverse-variance weighted (IVW), MR-Egger, and weighted median methods to evaluate the causal association between these factors. MR-Egger and MR-PRESSO were used for sensitivity analysis to test and correct horizontal pleiotropy. RESULTS A total of 731 immune traits were analyzed for association with PD using three MR methods. After adjustment for FDR, we observed four peripheral immunological features associated with PD using the IVW method, including expression of CX3CR1 on monocytes [OR: 0.85, 95% CI: (0.81, 0.91), P = 6.56E-07] and CX3CR1 on CD14+CD16+ monocytes [OR: 0.87, 95% CI: (0.82, 0.93), P = 9.95E-06]. CONCLUSIONS Our study further revealed the important role of monocytes in PD and indicated that CX3CR1 expression on monocytes is associated with a reduced risk of PD.
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Affiliation(s)
- Jie Tian
- Zheng Zhou Railway Vocational and Technical College, Zhengzhou, Henan, China
| | - Chunyan Zuo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Jingjing Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Dongrui Ma
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Changhe Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
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Cao L, Yin J, Du G, Yang Q, Huang Y. Identifying and verifying Huntington's disease subtypes: Clinical features, neuroimaging, and cytokine changes. Brain Behav 2024; 14:e3469. [PMID: 38494708 PMCID: PMC10945031 DOI: 10.1002/brb3.3469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/26/2024] [Accepted: 02/29/2024] [Indexed: 03/19/2024] Open
Abstract
AIMS Huntington's disease (HD) is a progressive neurodegenerative disorder with heterogeneous clinical manifestations. Identifying distinct clinical clusters and their relevant biomarkers could elucidate the underlying disease pathophysiology. METHODS Following the Enroll-HD program initiated in 2018.09, we have recruited 104 HD patients (including 21 premanifest) and 31 health controls at Beijing Tiantan Hospital. Principal components analysis and k-means cluster analysis were performed to determine HD clusters. Chi-square test, one-way ANOVA, and covariance were used to identify features among these clusters. Furthermore, plasma cytokines levels and brain structural imaging were used as biomarkers to delineate the clinical features of each cluster. RESULTS Three clusters were identified. Cluster 1 demonstrated the most severe motor and nonmotor symptoms except for chorea, the lowest whole brain volume, the plasma levels of IL-2 were higher and significantly associated with cluster 1. Cluster 2 was characterized with the most severe chorea and the largest pallidum volume. Cluster 3 had the most benign motor symptoms but moderate psychiatric problems. CONCLUSION We have identified three HD clusters via clinical manifestations with distinct biomarkers. Our data shed light on better understanding about the pathophysiology of HD.
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Affiliation(s)
- Ling‐Xiao Cao
- China National Clinical Research Center for Neurological DiseasesBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
- Department of NeurologyBeijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Jin‐Hui Yin
- China National Clinical Research Center for Neurological DiseasesBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
- Department of NeurologyBeijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Gang Du
- China National Clinical Research Center for Neurological DiseasesBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
- Department of NeurologyBeijing Tiantan HospitalCapital Medical UniversityBeijingChina
- Department of NeurologyThe Third People's Hospital of Longgang DistrictShenzhenChina
| | - Qing Yang
- China National Clinical Research Center for Neurological DiseasesBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
- Department of NeurologyBeijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Yue Huang
- China National Clinical Research Center for Neurological DiseasesBeijing Tiantan Hospital, Capital Medical UniversityBeijingChina
- Department of NeurologyBeijing Tiantan HospitalCapital Medical UniversityBeijingChina
- Pharmacology Department, School of Biomedical Sciences, Faculty of Medicine and HealthUNSW SydneySydneyAustralia
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Šubert M, Novotný M, Tykalová T, Hlavnička J, Dušek P, Růžička E, Škrabal D, Pelletier A, Postuma RB, Montplaisir J, Gagnon JF, Galbiati A, Ferini-Strambi L, Marelli S, St Louis EK, Timm PC, Teigen LN, Janzen A, Oertel W, Heim B, Holzknecht E, Stefani A, Högl B, Dauvilliers Y, Evangelista E, Šonka K, Rusz J. Spoken Language Alterations can Predict Phenoconversion in Isolated Rapid Eye Movement Sleep Behavior Disorder: A Multicenter Study. Ann Neurol 2024; 95:530-543. [PMID: 37997483 DOI: 10.1002/ana.26835] [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: 07/06/2023] [Revised: 11/13/2023] [Accepted: 11/20/2023] [Indexed: 11/25/2023]
Abstract
OBJECTIVE This study assessed the relationship between speech and language impairment and outcome in a multicenter cohort of isolated/idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD). METHODS Patients with iRBD from 7 centers speaking Czech, English, German, French, and Italian languages underwent a detailed speech assessment at baseline. Story-tale narratives were transcribed and linguistically annotated using fully automated methods based on automatic speech recognition and natural language processing algorithms, leading to the 3 distinctive linguistic and 2 acoustic patterns of language deterioration and associated composite indexes of their overall severity. Patients were then prospectively followed and received assessments for parkinsonism or dementia during follow-up. The Cox proportional hazard was performed to evaluate the predictive value of language patterns for phenoconversion over a follow-up period of 5 years. RESULTS Of 180 patients free of parkinsonism or dementia, 156 provided follow-up information. After a mean follow-up of 2.7 years, 42 (26.9%) patients developed neurodegenerative disease. Patients with higher severity of linguistic abnormalities (hazard ratio [HR = 2.35]) and acoustic abnormalities (HR = 1.92) were more likely to develop a defined neurodegenerative disease, with converters having lower content richness (HR = 1.74), slower articulation rate (HR = 1.58), and prolonged pauses (HR = 1.46). Dementia-first (n = 16) and parkinsonism-first with mild cognitive impairment (n = 9) converters had higher severity of linguistic abnormalities than parkinsonism-first with normal cognition converters (n = 17). INTERPRETATION Automated language analysis might provide a predictor of phenoconversion from iRBD into synucleinopathy subtypes with cognitive impairment, and thus can be used to stratify patients for neuroprotective trials. ANN NEUROL 2024;95:530-543.
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Affiliation(s)
- Martin Šubert
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Michal Novotný
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Tereza Tykalová
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Jan Hlavnička
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Petr Dušek
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Evžen Růžička
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Dominik Škrabal
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Amelie Pelletier
- Department of Neurology, Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, Quebec, Canada
- Center for Advanced Research in Sleep Medicine, CIUSSS-NÎM - Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada
| | - Ronald B Postuma
- Department of Neurology, Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, Quebec, Canada
- Center for Advanced Research in Sleep Medicine, CIUSSS-NÎM - Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada
| | - Jacques Montplaisir
- Center for Advanced Research in Sleep Medicine, CIUSSS-NÎM - Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada
| | - Jean-François Gagnon
- Center for Advanced Research in Sleep Medicine, CIUSSS-NÎM - Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada
| | - Andrea Galbiati
- Sleep Disorders Center, Division of Neuroscience, Ospedale San Raffaele, Università Vita-Salute, Milan, Italy
- Department of Psychology, "Vita-Salute" San Raffaele University, Milan, Italy
| | - Luigi Ferini-Strambi
- Sleep Disorders Center, Division of Neuroscience, Ospedale San Raffaele, Università Vita-Salute, Milan, Italy
- Department of Psychology, "Vita-Salute" San Raffaele University, Milan, Italy
| | - Sara Marelli
- Sleep Disorders Center, Division of Neuroscience, Ospedale San Raffaele, Università Vita-Salute, Milan, Italy
| | - Erik K St Louis
- Mayo Center for Sleep Medicine, and Sleep Behavior and Neurophysiology Research Laboratory, Departments of Neurology and Medicine, Division of Pulmonary and Critical Care Medicine Mayo Clinic College of Medicine and Science Rochester, Rochester, MN, USA
- Mayo Clinic Health System Southwest Wisconsin, La Crosse, WI, USA
| | - Paul C Timm
- Mayo Center for Sleep Medicine, and Sleep Behavior and Neurophysiology Research Laboratory, Departments of Neurology and Medicine, Division of Pulmonary and Critical Care Medicine Mayo Clinic College of Medicine and Science Rochester, Rochester, MN, USA
| | - Luke N Teigen
- Mayo Center for Sleep Medicine, and Sleep Behavior and Neurophysiology Research Laboratory, Departments of Neurology and Medicine, Division of Pulmonary and Critical Care Medicine Mayo Clinic College of Medicine and Science Rochester, Rochester, MN, USA
| | - Annette Janzen
- Department of Neurology, Philipps University Marburg, Marburg, Germany
| | - Wolfgang Oertel
- Department of Neurology, Philipps University Marburg, Marburg, Germany
| | - Beatrice Heim
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Evi Holzknecht
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Ambra Stefani
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Birgit Högl
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Yves Dauvilliers
- National Reference Network for Narcolepsy, Sleep-Wake Disorder Unit, Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier, INSERM, University of Montpellier, Montpellier, France
| | - Elisa Evangelista
- National Reference Network for Narcolepsy, Sleep-Wake Disorder Unit, Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier, INSERM, University of Montpellier, Montpellier, France
| | - Karel Šonka
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Jan Rusz
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
- Department of Neurology & ARTORG Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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Kawabata K, Djamshidian A, Bagarinao E, Weintraub D, Seppi K, Poewe W. Cognitive dysfunction in de novo Parkinson disease: Remitting vs. progressive cognitive impairment. Parkinsonism Relat Disord 2024; 120:105984. [PMID: 38198926 DOI: 10.1016/j.parkreldis.2023.105984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 11/03/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024]
Abstract
INTRODUCTION Parkinson's disease (PD) exhibits divergent cognitive trajectories; however, the factors contributing to these variations remain elusive. This study aimed to examine the clinical features of patients with different long-term cognitive trajectories in de novo PD over a five-year follow-up. METHODS We analyzed 258 patients who completed every annual evaluation for five years. According to the Montreal Cognitive Assessment (MoCA) scores, we classified patients into three groups: cognitively normal (n = 118, CN), remitting MoCA decline (n = 74, RMD), and progressive MoCA decline (n = 66, PMD). RESULTS The RMD group was associated with lower olfactory scores (Odds Ratio (OR) = 0.958, p = 0.040), whereas PMD was associated with higher depression scores (OR = 1.158, p = 0.045), probable RBD (OR = 3.169, p = 0.002), older age (OR = 1.132, p < 0.001) and lower educational attainment (OR = 0.828, p = 0.004). PMD had higher neurofilament light chain protein values than CN and RMD (p = 0.006, 0.015, respectively). Longitudinally, PMD showed a greater decline in all cognitive scores and hippocampus volumes (p = 0.004). Meanwhile, RMD exhibited intermediate cognitive and volumetric trajectories between CN and PMD and displayed worse score changes in memory tasks than CN. CONCLUSIONS While PMD exhibited known risk factors for cognitive impairment, along with worse cognitive performance and hippocampal volume decline, RMD displayed baseline lower olfactory scores and intermediate cognitive and hippocampal volume decline between the two groups. These findings suggest individuals in RMD may still be at risk for cognitive deficits. However, further long-term follow-up data are needed to unravel the determinants and dynamics of cognitive functions.
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Affiliation(s)
- Kazuya Kawabata
- Department of Neurology, Medical University Innsbruck, Innsbruck, Austria; Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan.
| | - Atbin Djamshidian
- Department of Neurology, Medical University Innsbruck, Innsbruck, Austria
| | - Epifanio Bagarinao
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Daniel Weintraub
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Parkinson's Disease Research, Education and Clinical Center (PADRECC), Philadelphia Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Klaus Seppi
- Department of Neurology, Medical University Innsbruck, Innsbruck, Austria
| | - Werner Poewe
- Department of Neurology, Medical University Innsbruck, Innsbruck, Austria
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Marras C, Fereshtehnejad SM, Berg D, Bohnen NI, Dujardin K, Erro R, Espay AJ, Halliday G, Van Hilten JJ, Hu MT, Jeon B, Klein C, Leentjens AFG, Mollenhauer B, Postuma RB, Rodríguez-Violante M, Simuni T, Weintraub D, Lawton M, Mestre TA. Transitioning from Subtyping to Precision Medicine in Parkinson's Disease: A Purpose-Driven Approach. Mov Disord 2024; 39:462-471. [PMID: 38243775 DOI: 10.1002/mds.29708] [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: 09/21/2023] [Revised: 11/29/2023] [Accepted: 12/18/2023] [Indexed: 01/21/2024] Open
Abstract
The International Parkinson and Movement Disorder Society (MDS) created a task force (TF) to provide a critical overview of the Parkinson's disease (PD) subtyping field and develop a guidance on future research in PD subtypes. Based on a literature review, we previously concluded that PD subtyping requires an ultimate alignment with principles of precision medicine, and consequently novel approaches were needed to describe heterogeneity at the individual patient level. In this manuscript, we present a novel purpose-driven framework for subtype research as a guidance to clinicians and researchers when proposing to develop, evaluate, or use PD subtypes. Using a formal consensus methodology, we determined that the key purposes of PD subtyping are: (1) to predict disease progression, for both the development of therapies (use in clinical trials) and prognosis counseling, (2) to predict response to treatments, and (3) to identify therapeutic targets for disease modification. For each purpose, we describe the desired product and the research required for its development. Given the current state of knowledge and data resources, we see purpose-driven subtyping as a pragmatic and necessary step on the way to precision medicine. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Connie Marras
- Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada
| | | | - Daniela Berg
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany
| | - Nicolaas I Bohnen
- Departments of Radiology & Neurology, University of Michigan, University of Michigan Udall Center, Ann Arbor, Michigan, USA
| | - Kathy Dujardin
- Center of Excellence for Parkinson's Disease, CHU Lille, Univ Lille, Inserm, Lille Neuroscience & Cognition, Lille, France
| | - Roberto Erro
- Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", Neuroscience Section, University of Salerno, Baronissi, Italy
| | - Alberto J Espay
- James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, Ohio, USA
| | - Glenda Halliday
- Brain and Mind Centre and Faculty of Medicine and Health School of Medical Sciences, University of Sydney, Sydney, New South Wales, Australia
| | - Jacobus J Van Hilten
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Michele T Hu
- Nuffield Department of Clinical Neurosciences, Oxford University and John Radcliffe Hospital, West Wing, Neurology Department, Level 3, Oxford, United Kingdom
| | - Beomseok Jeon
- Department of Neurology, Seoul National University Hospital, Seoul, South Korea
| | - Christine Klein
- Institute of Neurogenetics, University of Luebeck, Luebeck, Germany
| | - Albert F G Leentjens
- Department of Psychiatry, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Brit Mollenhauer
- Paracelsus-Elena-Klinik, Kassel, Department of Neurology, University Medical Center Goettingen, Kassel, Germany
| | - Ronald B Postuma
- Department of Neurology, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | | | - Tanya Simuni
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Daniel Weintraub
- Departments of Psychiatry and Neurology, Perelman School of Medicine at the University of Pennsylvania; Parkinson's Disease Research, Education and Clinical Center (PADRECC), Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Michael Lawton
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tiago A Mestre
- Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada
- Parkinson's Disease and Movement Disorders Center, Division of Neurology, Department of Medicine, The Ottawa Hospital Research Institute, The University of Ottawa Brain and Research Institute, Ottawa, Ontario, Canada
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Johansson ME, Toni I, Kessels RPC, Bloem BR, Helmich RC. Clinical severity in Parkinson's disease is determined by decline in cortical compensation. Brain 2024; 147:871-886. [PMID: 37757883 PMCID: PMC10907095 DOI: 10.1093/brain/awad325] [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: 03/24/2023] [Revised: 08/02/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
Dopaminergic dysfunction in the basal ganglia, particularly in the posterior putamen, is often viewed as the primary pathological mechanism behind motor slowing (i.e. bradykinesia) in Parkinson's disease. However, striatal dopamine loss fails to account for interindividual differences in motor phenotype and rate of decline, implying that the expression of motor symptoms depends on additional mechanisms, some of which may be compensatory in nature. Building on observations of increased motor-related activity in the parieto-premotor cortex of Parkinson patients, we tested the hypothesis that interindividual differences in clinical severity are determined by compensatory cortical mechanisms and not just by basal ganglia dysfunction. Using functional MRI, we measured variability in motor- and selection-related brain activity during a visuomotor task in 353 patients with Parkinson's disease (≤5 years disease duration) and 60 healthy controls. In this task, we manipulated action selection demand by varying the number of possible actions that individuals could choose from. Clinical variability was characterized in two ways. First, patients were categorized into three previously validated, discrete clinical subtypes that are hypothesized to reflect distinct routes of α-synuclein propagation: diffuse-malignant (n = 42), intermediate (n = 128) or mild motor-predominant (n = 150). Second, we used the scores of bradykinesia severity and cognitive performance across the entire sample as continuous measures. Patients showed motor slowing (longer response times) and reduced motor-related activity in the basal ganglia compared with controls. However, basal ganglia activity did not differ between clinical subtypes and was not associated with clinical scores. This indicates a limited role for striatal dysfunction in shaping interindividual differences in clinical severity. Consistent with our hypothesis, we observed enhanced action selection-related activity in the parieto-premotor cortex of patients with a mild-motor predominant subtype, both compared to patients with a diffuse-malignant subtype and controls. Furthermore, increased parieto-premotor activity was related to lower bradykinesia severity and better cognitive performance, which points to a compensatory role. We conclude that parieto-premotor compensation, rather than basal ganglia dysfunction, shapes interindividual variability in symptom severity in Parkinson's disease. Future interventions may focus on maintaining and enhancing compensatory cortical mechanisms, rather than only attempting to normalize basal ganglia dysfunction.
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Affiliation(s)
- Martin E Johansson
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Centre of Expertise for Parkinson & Movement Disorders, 6525 EN Nijmegen, The Netherlands
| | - Ivan Toni
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 EN Nijmegen, The Netherlands
| | - Roy P C Kessels
- Department of Medical Psychology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Radboudumc Alzheimer Center, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Vincent van Gogh Institute for Psychiatry, 5803 AC Venray, The Netherlands
| | - Bastiaan R Bloem
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Centre of Expertise for Parkinson & Movement Disorders, 6525 EN Nijmegen, The Netherlands
| | - Rick C Helmich
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Centre of Expertise for Parkinson & Movement Disorders, 6525 EN Nijmegen, The Netherlands
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Tang C, Sun R, Xue K, Wang M, Liang S, Kambey P, Shi M, Wu C, Chen G, Gao D. Distinct serum GDNF coupling with brain structural and functional changes underlies cognitive status in Parkinson's disease. CNS Neurosci Ther 2024; 30:e14461. [PMID: 37718594 PMCID: PMC10916445 DOI: 10.1111/cns.14461] [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: 03/22/2023] [Revised: 07/14/2023] [Accepted: 08/25/2023] [Indexed: 09/19/2023] Open
Abstract
AIM Aberrations in brain connections are implicated in the pathogenesis of Parkinson's disease (PD). We previously demonstrated that Glial cell-derived neurotrophic factor (GDNF) reduction is associated with cognition decline. Nonetheless, it is elusive if the pattern of brain topological connectivity differed across PD with divergent serum GDNF levels, and the accompanying profile of cognitive deficits has yet to be determined. METHODS We collected data on the participants' cognition, demographics, and serum GDNF levels. Participants underwent 3.0T magnetic resonance imaging, and we assessed the degree centrality, brain network topology, and cortical thickness of the healthy control (HC) (n = 25), PD-high-GDNF (n = 19), and PD-low-GDNF (n = 19) groups using graph-theoretic measures of resting-state functional MRI to reveal how much brain connectivity varies and its clinical correlates, as well as to determine factors predicting the cognitive status in PD. RESULTS The results show different network properties between groups. Degree centrality abnormalities were found in the right inferior frontal gyrus and right parietal lobe postcentral gyrus, linked with cognition scores. The two aberrant clusters serve as a potentially powerful signal for determining whether a patient has PD and the patient's cognition level after integrating with GDNF, duration, and dopamine dosage. Moreover, we found a significant positive relationship between the thickness of the left caudal middle frontal lobe and a plethora of cognitive domains. Further discriminant analysis revealed that the cortical thickness of this region could distinguish PD patients from healthy controls. The mental state evaluation will also be more precise when paired with GDNF and duration. CONCLUSION Our findings reveal that the topological features of brain networks and cortical thickness are altered in PD patients with cognitive deficits. The above change, accompanied by the serum GDNF, may have merit as a diagnosis marker for PD and, arguably, cognition status.
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Affiliation(s)
- Chuanxi Tang
- Department of Neurobiology, Xuzhou Key Laboratory of NeurobiologyXuzhou Medical UniversityXuzhouJiangsuChina
| | - Ruiao Sun
- Department of Neurobiology, Xuzhou Key Laboratory of NeurobiologyXuzhou Medical UniversityXuzhouJiangsuChina
| | - Ke Xue
- Department of Neurobiology, Xuzhou Key Laboratory of NeurobiologyXuzhou Medical UniversityXuzhouJiangsuChina
| | - Mengying Wang
- Department of Epidemiology and Biostatistics, School of Public HealthPeking University Health Science CenterBeijingChina
| | - Sijie Liang
- Department of RehabilitationThe Affiliated Hospital of Xuzhou Medical UniversityXuzhouJiangsuChina
| | - Piniel Alphayo Kambey
- Department of Neurobiology, Xuzhou Key Laboratory of NeurobiologyXuzhou Medical UniversityXuzhouJiangsuChina
| | - Mingyu Shi
- Department of Neurobiology, Xuzhou Key Laboratory of NeurobiologyXuzhou Medical UniversityXuzhouJiangsuChina
| | - Changyu Wu
- School of Medical ImagingXuzhou Medical UniversityXuzhouJiangsuChina
| | - Gang Chen
- Department of NeurologyShuyang Hospital of Traditional Chinese MedicineSuqianJiangsuChina
| | - Dianshuai Gao
- Department of Neurobiology, Xuzhou Key Laboratory of NeurobiologyXuzhou Medical UniversityXuzhouJiangsuChina
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Pan G, Jiang Y, Zhang W, Zhang X, Wang L, Cheng W. Identification of Parkinson's disease subtypes with distinct brain atrophy progression and its association with clinical progression. PSYCHORADIOLOGY 2024; 4:kkae002. [PMID: 38666137 PMCID: PMC10953620 DOI: 10.1093/psyrad/kkae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/27/2024] [Accepted: 02/23/2024] [Indexed: 04/28/2024]
Abstract
Background Parkinson's disease (PD) patients suffer from progressive gray matter volume (GMV) loss, but whether distinct patterns of atrophy progression exist within PD are still unclear. Objective This study aims to identify PD subtypes with different rates of GMV loss and assess their association with clinical progression. Methods This study included 107 PD patients (mean age: 60.06 ± 9.98 years, 70.09% male) with baseline and ≥ 3-year follow-up structural MRI scans. A linear mixed-effects model was employed to assess the rates of regional GMV loss. Hierarchical cluster analysis was conducted to explore potential subtypes based on individual rates of GMV loss. Clinical score changes were then compared across these subtypes. Results Two PD subtypes were identified based on brain atrophy rates. Subtype 1 (n = 63) showed moderate atrophy, notably in the prefrontal and lateral temporal lobes, while Subtype 2 (n = 44) had faster atrophy across the brain, particularly in the lateral temporal region. Furthermore, subtype 2 exhibited faster deterioration in non-motor (MDS-UPDRS-Part Ⅰ, β = 1.26 ± 0.18, P = 0.016) and motor (MDS-UPDRS-Part Ⅱ, β = 1.34 ± 0.20, P = 0.017) symptoms, autonomic dysfunction (SCOPA-AUT, β = 1.15 ± 0.22, P = 0.043), memory (HVLT-Retention, β = -0.02 ± 0.01, P = 0.016) and depression (GDS, β = 0.26 ± 0.083, P = 0.019) compared to subtype 1. Conclusion The study has identified two PD subtypes with distinct patterns of atrophy progression and clinical progression, which may have implications for developing personalized treatment strategies.
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Affiliation(s)
- Guoqing Pan
- School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004, China
- Fudan ISTBI—ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua 321004, China
| | - Yuchao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Shanghai 201210, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Shanghai 201210, China
| | - Xuejuan Zhang
- School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004, China
- Fudan ISTBI—ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua 321004, China
| | - Linbo Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Shanghai 201210, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China
- Zhangjiang Fudan International Innovation Center, Shanghai 201210, China
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai 200032, China
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Li J, Duan S, Yang J, Zheng H, Yuan Y, Tang M, Wang Y, Liu Y, Xia Z, Luo H, Xu Y. Detection of skin α-synuclein using RT-QuIC as a diagnostic biomarker for Parkinson's disease in the Chinese population. Eur J Med Res 2024; 29:114. [PMID: 38336827 PMCID: PMC10854029 DOI: 10.1186/s40001-024-01705-x] [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: 09/21/2023] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Several studies have indicated that skin holds promise as a potential sample for detecting pathological α-Syn and serving as a diagnostic biomarker for α-synucleinopathies. Despite reports in Chinese PD patients, comprehensive research on skin α-Syn detection using RT-QuIC is lacking. OBJECTIVE This study aimed to evaluate the diagnostic performance of skin samples using RT-QuIC from PD patients in the Chinese population. METHODS Patients with sporadic PD and controls were included according to the British PD Association Brain Bank diagnostic criteria. The seeding activity of misfolded α-Syn in these skin samples was detected using the RT-QuIC assay after protein extraction. Biochemical and morphological analyses of RT-QuIC products were conducted by atomic force microscopy, transmission electron microscopy, Congo red staining, and dot blot analysis. RESULT 30 patients clinically diagnosed with PD and 28 controls with non-α-synucleinopathies were included in this study. 28 of 30 PD patients demonstrated positive α-Syn seeding activity by RT-QuIC assay. In contrast, no α-Syn seeding activity was detected in the 28 control samples, with an overall sensitivity and specificity of 93.3% and 100%, respectively (P < 0.001). Biochemical characterization of the RT-QuIC product indicated fibrillary α-Syn species in PD-seeded reactions, while control samples failed in the conversion of recombinant α-Syn substrate. CONCLUSION This study applied RT-QuIC technology to identify misfolded α-Syn seeding activity in skin samples from Chinese PD patients, demonstrating high specificity and sensitivity. Skin α-Syn RT-QuIC is expected to be a reliable approach for the diagnosis of PD.
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Affiliation(s)
- Jiaqi Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Suying Duan
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Jing Yang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Honglin Zheng
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- The Academy of Medical Sciences of Zhengzhou University, Zhengzhou University, Jian-She East Road, Zhengzhou, 450000, Henan, China
| | - Yanpeng Yuan
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Mibo Tang
- Department of Geriatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Yanlin Wang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Yutao Liu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Zongping Xia
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Haiyang Luo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China.
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China.
| | - Yuming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China.
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China.
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan, China.
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Zhao H, Kwon O, Cha J, Jung IC, Jun P, Jang JY, Jang JH. Exploring Traditional Medicine Diagnostic Classification for Parkinson's Disease Using Hierarchical Clustering. Complement Med Res 2024; 31:160-174. [PMID: 38330930 DOI: 10.1159/000536047] [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: 05/15/2023] [Accepted: 12/27/2023] [Indexed: 02/10/2024]
Abstract
INTRODUCTION Personalized diagnosis and therapy for Parkinson's disease (PD) are needed due to the clinical heterogeneity of PD. Syndrome differentiation (SD) in traditional medicine (TM) is a diagnostic method for customized therapy that comprehensively analyzes various symptoms and systemic syndromes. However, research identifying PD classification based on SD is limited. METHODS Ten electronic databases were systematically searched from inception to August 10, 2021. Clinical indicators, including 380 symptoms, 98 TM signs, and herbal medicine for PD diagnosed with SD, were extracted from 197 articles; frequency statistics on clinical indicators were conducted to classify several subtypes using hierarchical clustering. RESULTS Four distinct cluster groups were identified, each characterized by significant cluster-specific clinical indicators with 95% confidence intervals of distribution. Subtype 2 had the most severe progression, longest progressive duration, and highest association with greater late-stage PD-associated motor symptoms, including postural instability and gait disturbance. The action properties of the herbal formula and original SD presented in the data sources for subtype 2 were associated with Yin deficiency syndrome. DISCUSSION/CONCLUSION Hierarchical clustering analysis distinguished various symptoms and TM signs among patients with PD. These newly identified PD subtypes may optimize the diagnosis and treatment with TM and facilitate prognosis prediction. Our findings serve as a cornerstone for evidence-based guidelines for TM diagnosis and treatment. Einleitung Eine personalisierte Diagnose und Therapie des Morbus Parkinson (MP) ist angesichts der ausgeprägten klinischen Heterogenität des MP unerlässlich. Die Syndromdifferenzierung (SD) ist in der traditionellen Medizin (TM) eine diagnostische Methode für eine maßgeschneiderte Therapie, bei der verschiedene Symptome und systemische Syndrome umfassend analysiert werden. Es liegen jedoch nur begrenzt Forschungsergebnisse in Bezug auf eine SD-basierte Klassifikation des MP vor. Methoden Zehn elektronische Datenbanken wurden systematisch durchsucht, von der Einrichtung bis zum 10. August 2021. Klinische Indikatoren einschließlich von 380 Symptomen, 98 TM-Zeichen sowie pflanzlichen Heilmitteln für mittels SD diagnostiziertem MP wurden aus 197 Artikeln extrahiert, und Häufigkeitsstatistiken der klinischen Indikatoren wurden erstellt, um mittels hierarchischem Clustering eine Reihe von Subtypen zu klassifizieren. Ergebnisse Vier verschiedene Cluster-Gruppen wurden identifiziert, die jeweils durch signifikante, Cluster-spezifische klinische Indikatoren mit 95% Konfidenzintervall der Verteilung gekennzeichnet waren. Subtyp 2 hatte den schwersten Verlauf, die längste Progressionsdauer und die stärkste Assoziation mit einem höheren Ausmaß von motorischen Symptomen des MP im Spätstadium, darunter Haltungsinstabilität und Gangstörungen. Die Wirkungseigenschaften der pflanzlichen Formulierung sowie die ursprüngliche SD, die in den Datenquellen für Subtyp 2 genannt wurden, waren mit Yin-Mangel-Syndrom assoziiert. Diskussion/Schlussfolgerung Die hierarchische Clustering-Analyse hob verschiedene Symptome und TM-Zeichen bei Patienten mit MP hervor. Die neu identifizierten MP-Subtypen könnten die Diagnose und Behandlung mittels TM optimieren und zur Prognoseerstellung beitragen. Unsere Ergebnisse sind ein Fundament für eine evidenzbasierte Leitlinie für die TM-Diagnostik und -Therapie.
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Affiliation(s)
- HuiYan Zhao
- KM Science Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
- Korean Convergence Medical Science, University of Science and Technology, School of Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Ojin Kwon
- KM Science Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Jiyun Cha
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
- Department of Internal Korean Medicine, College of Korean Medicine, Daejeon University, Daejeon, Republic of Korea
| | - In Chul Jung
- Department of Oriental Neuropsychiatry, College of Korean Medicine, Daejeon University, Daejeon, Republic of Korea
| | - Purumea Jun
- KM Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Jae Young Jang
- School of Electrical, Electronics, and Communication Engineering, Korea University of Technology and Education, Cheonan, Republic of Korea
| | - Jung-Hee Jang
- KM Science Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
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Theis H, Pavese N, Rektorová I, van Eimeren T. Imaging Biomarkers in Prodromal and Earliest Phases of Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2024:JPD230385. [PMID: 38339941 DOI: 10.3233/jpd-230385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2024]
Abstract
Assessing imaging biomarker in the prodromal and early phases of Parkinson's disease (PD) is of great importance to ensure an early and safe diagnosis. In the last decades, imaging modalities advanced and are now able to assess many different aspects of neurodegeneration in PD. MRI sequences can measure iron content or neuromelanin. Apart from SPECT imaging with Ioflupane, more specific PET tracers to assess degeneration of the dopaminergic system are available. Furthermore, metabolic PET patterns can be used to anticipate a phenoconversion from prodromal PD to manifest PD. In this regard, it is worth mentioning that PET imaging of inflammation will gain significance. Molecular imaging of neurotransmitters like serotonin, noradrenaline and acetylcholine shed more light on non-motor symptoms. Outside of the brain, molecular imaging of the heart and gut is used to measure PD-related degeneration of the autonomous nervous system. Moreover, optical coherence tomography can noninvasively detect degeneration of retinal fibers as a potential biomarker in PD. In this review, we describe these state-of-the-art imaging modalities in early and prodromal PD and point out in how far these techniques can and will be used in the future to pave the way towards a biomarker-based staging of PD.
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Affiliation(s)
- Hendrik Theis
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, Multimodal Neuroimaging Group, Cologne, Germany
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany
| | - Nicola Pavese
- Aarhus University, Institute of Clinical Medicine, Department of Nuclear Medicine & PET, Aarhus N, Denmark
- Newcastle University, Translational and Clinical Research Institute, Newcastle upon Tyne, United Kingdom
| | - Irena Rektorová
- Masaryk University, Faculty of Medicine and St. Anne's University Hospital, International Clinical Research Center, ICRC, Brno, Czech Republic
- Masaryk University, Faculty of Medicine and St. Anne's University Hospital, First Department of Neurology, Brno, Czech Republic
- Masaryk University, Applied Neuroscience Research Group, Central European Institute of Technology - CEITEC, Brno, Czech Republic
| | - Thilo van Eimeren
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, Multimodal Neuroimaging Group, Cologne, Germany
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany
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Liu L, Shi Z, Gan J, Liu S, Wen C, Yang Y, Yang F, Ji Y. Characterization of de novo Dementia with Lewy Body with different duration of rapid eye movement sleep behavior disorder. Sleep Med 2024; 114:101-108. [PMID: 38176204 DOI: 10.1016/j.sleep.2023.12.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/06/2023] [Accepted: 12/25/2023] [Indexed: 01/06/2024]
Abstract
BACKGROUND Cognitive disorder, parkinsonism, autonomic dysfunction (AuD) and rapid eye movement sleep behavior disorder (RBD) can occur prior to or simultaneously with Dementia with Lewy Body (DLB) onset. RBD is generally linked with progressive neurodegenerative traits. However, associations between RBD with DLB, RBD without DLB, and RBD duration effects on DLB symptoms remain unclear. OBJECTIVES To examine DLB symptom frequency and subtypes in RBD, and explore the effects of different RBD onset times on symptoms in de novo DLB patients. METHODS In this multicenter investigation, we consecutively recruited 271 de novo DLB patients. All had standardized clinical and comprehensive neuropsychological evaluations. Subgroup analyses, performed based on the duration of RBD confirmed by polysomnography before the DLB diagnosis, we compared the proportion of patients with cognitive impairment, parkinsonism, and AuD features between groups. RESULTS Parkinsonism and AuD incidences were significantly elevated in DLB patients with RBD when compared with patients without RBD. Subgroup analyses indicated no significant differences in parkinsonism between DLB patients who developed RBD ≥10 years prior to the DLB diagnosis and DLB patients without RBD. The incidence of non-tremor-predominant parkinsonism and AuD was significantly higher in DLB patients whose RBD duration before the DLB diagnosis was <10 years when compared with DLB patients without RBD. CONCLUSIONS We identified significant symptom and phenotypic variability between DLB patients with and without RBD. Also, different RBD duration effects before the DLB diagnosis had a significant impact on symptomatic phenotypes, suggesting the existence of a slowly progressive DLB neurodegenerative subtype.
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Affiliation(s)
- Lixin Liu
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China; The Psycho Department of Beijing Geriatric Hospital, Beijing, China
| | - Zhihong Shi
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin Key Laboratory of Cerebrovascular and of Neurodegenerative Diseases, Tianjin Dementia Institute, Tianjin, China
| | - Jinghuan Gan
- Department of Neurology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Shuai Liu
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin Key Laboratory of Cerebrovascular and of Neurodegenerative Diseases, Tianjin Dementia Institute, Tianjin, China
| | - Chen Wen
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yaqi Yang
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China
| | - Fan Yang
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China
| | - Yong Ji
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin, China; Department of Neurology, Tianjin Huanhu Hospital, Tianjin Key Laboratory of Cerebrovascular and of Neurodegenerative Diseases, Tianjin Dementia Institute, Tianjin, China.
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Dan XJ, Wang YW, Sun JY, Gao LL, Chen X, Yang XY, Xu EH, Ma JH, Yan CG, Wu T, Chan P. Reorganization of intrinsic functional connectivity in early-stage Parkinson's disease patients with probable REM sleep behavior disorder. NPJ Parkinsons Dis 2024; 10:5. [PMID: 38172178 PMCID: PMC10764752 DOI: 10.1038/s41531-023-00617-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 12/07/2023] [Indexed: 01/05/2024] Open
Abstract
REM sleep behavior disorder (RBD) symptoms in Parkinson's disease (PD) suggest both a clinically and pathologically malignant subtype. However, whether RBD symptoms are associated with alterations in the organization of whole-brain intrinsic functional networks in PD, especially at early disease stages, remains unclear. Here we use resting-state functional MRI, coupled with graph-theoretical approaches and network-based statistics analyses, and validated with large-scale network analyses, to characterize functional brain networks and their relationship with clinical measures in early PD patients with probable RBD (PD+pRBD), early PD patients without probable RBD (PD-pRBD) and healthy controls. Thirty-six PD+pRBD, 57 PD-pRBD and 71 healthy controls were included in the final analyses. The PD+pRBD group demonstrated decreased global efficiency (t = -2.036, P = 0.0432) compared to PD-pRBD, and decreased network efficiency, as well as comprehensively disrupted nodal efficiency and whole-brain networks (all eight networks, but especially in the sensorimotor, default mode and visual networks) compared to healthy controls. The PD-pRBD group showed decreased nodal degree in right ventral frontal cortex and more affected edges in the frontoparietal and ventral attention networks compared to healthy controls. Furthermore, the assortativity coefficient was negatively correlated with Montreal cognitive assessment scores in the PD+pRBD group (r = -0.365, P = 0.026, d = 0.154). The observation of altered whole-brain functional networks and its correlation with cognitive function in PD+pRBD suggest reorganization of the intrinsic functional connectivity to maintain the brain function in the early stage of the disease. Future longitudinal studies following these alterations along disease progression are warranted.
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Affiliation(s)
- Xiao-Juan Dan
- Department of Neurology, Xuanwu Hospital of Capital Medical University, 100053, Beijing, China
- Key Laboratory on Neurodegenerative Disorders of Ministry of Education, Key Laboratory on Parkinson's Disease of Beijing, 100053, Beijing, China
| | - Yu-Wei Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, 100101, Beijing, China
| | - Jun-Yan Sun
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, 100070, Beijing, China
| | - Lin-Lin Gao
- Department of Neurobiology, Xuanwu Hospital of Capital Medical University, 100053, Beijing, China
| | - Xiao Chen
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, 100101, Beijing, China
| | - Xue-Ying Yang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, 100101, Beijing, China
| | - Er-He Xu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, 100053, Beijing, China
| | - Jing-Hong Ma
- Department of Neurology, Xuanwu Hospital of Capital Medical University, 100053, Beijing, China
| | - Chao-Gan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, 100101, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, 100101, Beijing, China
| | - Tao Wu
- Center for Movement Disorders, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, 100070, Beijing, China.
| | - Piu Chan
- Department of Neurology, Xuanwu Hospital of Capital Medical University, 100053, Beijing, China.
- Key Laboratory on Neurodegenerative Disorders of Ministry of Education, Key Laboratory on Parkinson's Disease of Beijing, 100053, Beijing, China.
- National Clinical Research Center for Geriatric Disorders, 100053, Beijing, China.
- Beijing Institute for Brain Disorders Parkinson's Disease Center, Advanced Innovation Center for Human Brain Protection, Capital Medical University, 100069, Beijing, China.
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Di Folco C, Couronné R, Arnulf I, Mangone G, Leu-Semenescu S, Dodet P, Vidailhet M, Corvol JC, Lehéricy S, Durrleman S. Charting Disease Trajectories from Isolated REM Sleep Behavior Disorder to Parkinson's Disease. Mov Disord 2024; 39:64-75. [PMID: 38006282 DOI: 10.1002/mds.29662] [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: 10/10/2022] [Revised: 10/03/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Clinical presentation and progression dynamics are variable in patients with Parkinson's disease (PD). Disease course mapping is an innovative disease modelling technique that summarizes the range of possible disease trajectories and estimates dimensions related to onset, sequence, and speed of progression of disease markers. OBJECTIVE To propose a disease course map for PD and investigate progression profiles in patients with or without rapid eye movement sleep behavioral disorders (RBD). METHODS Data of 919 PD patients and 88 isolated RBD patients from three independent longitudinal cohorts were analyzed (follow-up duration = 5.1; 95% confidence interval, 1.1-8.1] years). Disease course map was estimated by using eight clinical markers (motor and non-motor symptoms) and four imaging markers (dopaminergic denervation). RESULTS PD course map showed that the first changes occurred in the contralateral putamen 13 years before diagnosis, followed by changes in motor symptoms, dysautonomia, sleep-all before diagnosis-and finally cognitive decline at the time of diagnosis. The model showed earlier disease onset, earlier non-motor and later motor symptoms, more rapid progression of cognitive decline in PD patients with RBD than PD patients without RBD. This pattern was even more pronounced in patients with isolated RBD with early changes in sleep, followed by cognition and non-motor symptoms and later changes in motor symptoms. CONCLUSIONS Our findings are consistent with the presence of distinct patterns of progression between patients with and without RBD. Understanding heterogeneity of PD progression is key to decipher the underlying pathophysiology and select homogeneous subgroups of patients for precision medicine. © 2023 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Cécile Di Folco
- Inria, Centre de Paris, Paris, France
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Raphaël Couronné
- Inria, Centre de Paris, Paris, France
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Isabelle Arnulf
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Graziella Mangone
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Smaranda Leu-Semenescu
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Pauline Dodet
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Marie Vidailhet
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Jean-Christophe Corvol
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stéphane Lehéricy
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stanley Durrleman
- Inria, Centre de Paris, Paris, France
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
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Joza S, Hu MT, Jung K, Kunz D, Arnaldi D, Lee J, Ferini‐Strambi L, Antelmi E, Sixel‐Döring F, De Cock VC, Montplaisir JY, Welch J, Kim H, Bes F, Mattioli P, Woo KA, Marelli S, Plazzi G, Mollenhauer B, Pelletier A, Razzaque J, Sunwoo J, Girtler N, Trenkwalder C, Gagnon J, Postuma RB. Prodromal dementia with Lewy bodies in REM sleep behavior disorder: A multicenter study. Alzheimers Dement 2024; 20:91-102. [PMID: 37461299 PMCID: PMC10917000 DOI: 10.1002/alz.13386] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/12/2023] [Indexed: 01/18/2024]
Abstract
INTRODUCTION Isolated/idiopathic rapid eye movement sleep behavior disorder (iRBD) is a powerful early predictor of dementia with Lewy bodies (DLB) and Parkinson's disease (PD). This provides an opportunity to directly observe the evolution of prodromal DLB and to identify which cognitive variables are the strongest predictors of evolving dementia. METHODS IRBD participants (n = 754) from 10 centers of the International RBD Study Group underwent annual neuropsychological assessment. Competing risk regression analysis determined optimal predictors of dementia. Linear mixed-effect models determined the annual progression of neuropsychological testing. RESULTS Reduced attention and executive function, particularly performance on the Trail Making Test Part B, were the strongest identifiers of early DLB. In phenoconverters, the onset of cognitive decline began up to 10 years prior to phenoconversion. Changes in verbal memory best differentiated between DLB and PD subtypes. DISCUSSION In iRBD, attention and executive dysfunction strongly predict dementia and begin declining several years prior to phenoconversion. HIGHLIGHTS Cognitive decline in iRBD begins up to 10 years prior to phenoconversion. Attention and executive dysfunction are the strongest predictors of dementia in iRBD. Decline in episodic memory best distinguished dementia-first from parkinsonism-first phenoconversion.
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Affiliation(s)
- Stephen Joza
- Department of NeurologyMontreal Neurological InstituteMontrealCanada
| | - Michele T. Hu
- Nuffield Department of Clinical Neurosciences, Division of Neurology and Oxford Parkinson's Disease CentreUniversity of OxfordOxfordUK
| | - Ki‐Young Jung
- Department of Neurology, Seoul National University College of MedicineSeoul National University HospitalSeoulRepublic of Korea
| | - Dieter Kunz
- Clinic for Sleep & ChronomedicineSt. Hedwig‐KrankenhausBerlinGermany
| | - Dario Arnaldi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), Clinical NeurologyUniversity of GenoaGenoaItaly
- IRCCS Ospedale Policlinico San MartinoGenoaItaly
| | - Jee‐Young Lee
- Department of NeurologySeoul National University College of MedicineSeoul Metropolitan Government‐Seoul National University Boramae Medical CenterSeoulSouth Korea
| | | | - Elena Antelmi
- DIMI Department of Engineering and Medicine of InnovationUniversity of VeronaVeronaItaly
| | - Friederike Sixel‐Döring
- Department of Neurology and Section on Clinical NeurosciencePhilipps University MarburgMarburgGermany
- Paracelsus Elena KlinikCentre for Movement DisordersKasselGermany
| | - Valérie Cochen De Cock
- EuroMov Digital Health in MotionUniversity of MontpellierIMT Mines AlesMontpellierFrance
- Department of Neurology and SleepBeau Soleil ClinicMontpellierFrance
| | - Jacques Y. Montplaisir
- Centre d’Études Avancées en Médecine du SommeilHôpital du Sacré‐Cœur de MontréalMontréalQuebecCanada
- Department of PsychologyUniversité du Québec à MontréalMontréalQuebecCanada
| | - Jessica Welch
- Nuffield Department of Clinical Neurosciences, Division of Neurology and Oxford Parkinson's Disease CentreUniversity of OxfordOxfordUK
| | - Han‐Joon Kim
- Department of Neurology, Seoul National University College of MedicineSeoul National University HospitalSeoulRepublic of Korea
| | - Frederik Bes
- Clinic for Sleep & ChronomedicineSt. Hedwig‐KrankenhausBerlinGermany
| | - Pietro Mattioli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), Clinical NeurologyUniversity of GenoaGenoaItaly
| | - Kyung Ah Woo
- Department of NeurologySeoul National University College of MedicineSeoul Metropolitan Government‐Seoul National University Boramae Medical CenterSeoulSouth Korea
| | - Sara Marelli
- Sleep Disorders CenterVita‐Salute San Raffaele UniversityMilanItaly
| | - Giuseppe Plazzi
- IRCCS Istituto delle Scienze Neurologiche di BolognaBolognaItaly
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio‐EmiliaModenaItaly
| | - Brit Mollenhauer
- Department of Neurology and Section on Clinical NeurosciencePhilipps University MarburgMarburgGermany
- Paracelsus Elena KlinikCentre for Movement DisordersKasselGermany
| | - Amelie Pelletier
- Department of NeurologyMontreal Neurological InstituteMontrealCanada
- Centre d’Études Avancées en Médecine du SommeilHôpital du Sacré‐Cœur de MontréalMontréalQuebecCanada
| | - Jamil Razzaque
- Nuffield Department of Clinical Neurosciences, Division of Neurology and Oxford Parkinson's Disease CentreUniversity of OxfordOxfordUK
| | - Jun‐Sang Sunwoo
- Department of NeurologyKangbuk Samsung HospitalSeoulRepublic of Korea
| | - Nicola Girtler
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), Clinical NeurologyUniversity of GenoaGenoaItaly
- IRCCS Ospedale Policlinico San MartinoGenoaItaly
| | - Claudia Trenkwalder
- Paracelsus Elena KlinikCentre for Movement DisordersKasselGermany
- Department of NeurosurgeryUniversity Medical Center GoettingenGöttingenGermany
| | - Jean‐François Gagnon
- Centre d’Études Avancées en Médecine du SommeilHôpital du Sacré‐Cœur de MontréalMontréalQuebecCanada
- Department of PsychologyUniversité du Québec à MontréalMontréalQuebecCanada
| | - Ronald B. Postuma
- Department of NeurologyMontreal Neurological InstituteMontrealCanada
- Centre d’Études Avancées en Médecine du SommeilHôpital du Sacré‐Cœur de MontréalMontréalQuebecCanada
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Hajebrahimi F, Budak M, Saricaoglu M, Temel Z, Demir TK, Hanoglu L, Yildirim S, Bayraktaroglu Z. Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment. Brain Behav 2024; 14:e3395. [PMID: 38376051 PMCID: PMC10808882 DOI: 10.1002/brb3.3395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/23/2023] [Accepted: 12/26/2023] [Indexed: 02/21/2024] Open
Abstract
INTRODUCTION Cognitive impairment (CI) is a significant non-motor symptoms in Parkinson's disease (PD) that often precedes the emergence of motor symptoms by several years. Patients with PD hypothetically progress from stages without CI (PD-normal cognition [NC]) to stages with Mild CI (PD-MCI) and PD dementia (PDD). CI symptoms in PD are linked to different brain regions and neural pathways, in addition to being the result of dysfunctional subcortical regions. However, it is still unknown how functional dysregulation correlates to progression during the CI. Neuroimaging techniques hold promise in discriminating CI stages of PD and further contribute to the biomarker formation of CI in PD. In this study, we explore disparities in the clinical assessments and resting-state functional connectivity (FC) among three CI stages of PD. METHODS We enrolled 88 patients with PD and 26 healthy controls (HC) for a cross sectional clinical study and performed intra- and inter-network FC analysis in conjunction with comprehensive clinical cognitive assessment. RESULTS Our findings underscore the significance of several neural networks, namely, the default mode network (DMN), frontoparietal network (FPN), dorsal attention network, and visual network (VN) and their inter-intra-network FC in differentiating between PD-MCI and PDD. Additionally, our results showed the importance of sensory motor network, VN, DMN, and salience network (SN) in the discriminating PD-NC from PDD. Finally, in comparison to HC, we found DMN, FPN, VN, and SN as pivotal networks for further differential diagnosis of CI stages of PD. CONCLUSION We propose that resting-state networks (RSN) can be a discriminating factor in distinguishing the CI stages of PD and progressing from PD-NC to MCI or PDD. The integration of clinical and neuroimaging data may enhance the early detection of PD in clinical settings and potentially prevent the disease from advancing to more severe stages.
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Affiliation(s)
- Farzin Hajebrahimi
- Functional Imaging and Cognitive‐Affective Neuroscience Lab (fINCAN), Research Institute for Health Sciences and Technologies (SABITA)Istanbul Medipol UniversityIstanbulTurkey
- Department of Physical Therapy and Rehabilitation, School of Health SciencesIstanbul Medipol UniversityIstanbulTurkey
- Department of Health Informatics, Rutgers University, School of Health ProfessionsRutgers Biomedical and Health SciencesNewarkNew JerseyUSA
| | - Miray Budak
- Functional Imaging and Cognitive‐Affective Neuroscience Lab (fINCAN), Research Institute for Health Sciences and Technologies (SABITA)Istanbul Medipol UniversityIstanbulTurkey
- Department of Ergotherapy, School of Health SciencesIstanbul Medipol UniversityIstanbulTurkey
- Center for Molecular and Behavioral NeuroscienceRutgers University‐NewarkNewarkNew JerseyUSA
| | - Mevhibe Saricaoglu
- Functional Imaging and Cognitive‐Affective Neuroscience Lab (fINCAN), Research Institute for Health Sciences and Technologies (SABITA)Istanbul Medipol UniversityIstanbulTurkey
- Program of Electroneurophysiology, Vocational SchoolIstanbul Medipol UniversityIstanbulTurkey
| | - Zeynep Temel
- Department of PsychologyFatih Sultan Mehmet Vakif UniversityIstanbulTurkey
| | - Tugce Kahraman Demir
- Program of Electroneurophysiology, Vocational SchoolBiruni UniversityIstanbulTurkey
| | - Lutfu Hanoglu
- Department of Neurology, School of MedicineIstanbul Medipol UniversityIstanbulTurkey
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA)Istanbul Medipol UniversityIstanbulTurkey
| | - Suleyman Yildirim
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA)Istanbul Medipol UniversityIstanbulTurkey
- Department of Medical Microbiology, International School of MedicineIstanbul Medipol UniversityIstanbulTurkey
| | - Zubeyir Bayraktaroglu
- Functional Imaging and Cognitive‐Affective Neuroscience Lab (fINCAN), Research Institute for Health Sciences and Technologies (SABITA)Istanbul Medipol UniversityIstanbulTurkey
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA)Istanbul Medipol UniversityIstanbulTurkey
- Department of Physiology, International School of MedicineIstanbul Medipol UniversityIstanbulTurkey
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48
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Deng X, Saffari SE, Xiao B, Ng SYE, Chia N, Choi X, Heng DL, Ng E, Xu Z, Tay KY, Au WL, Tan EK, Tan LC. Disease Progression of Data-Driven Subtypes of Parkinson's Disease: 5-Year Longitudinal Study from the Early Parkinson's Disease Longitudinal Singapore (PALS) Cohort. JOURNAL OF PARKINSON'S DISEASE 2024; 14:1051-1059. [PMID: 38848193 PMCID: PMC11307075 DOI: 10.3233/jpd-230209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/24/2024] [Indexed: 06/09/2024]
Abstract
Background The detailed trajectory of data-driven subtypes in Parkinson's disease (PD) within Asian cohorts remains undisclosed. Objective To evaluate the motor, non-motor symptom (NMS) progression among the data-driven PD clusters. Methods In this 5-year longitudinal study, NMS scale (NMSS), Hospital Anxiety Depression Scale (HADS), and Epworth sleepiness scale (ESS) were carried out annually to monitor NMS progression. H& Y staging scale, MDS-UPDRS part III motor score, and postural instability gait difficulty (PIGD) score were assessed annually to evaluate disease severity and motor progression. Five cognitive standardized scores were used to assess detailed cognitive progression. Linear mixed model was performed to assess the annual progression rates of the longitudinal outcomes. Results Two hundred and six early PD patients, consisting of 43 patients in cluster A, 98 patients in cluster B and 65 subjects in cluster C. Cluster A (severe subtype) had significantly faster progression slope in NMSS Domain 3 (mood/apathy) score (p = 0.01), NMSS Domain 4 (perceptual problems) score (p = 0.02), NMSS Domain 7 (urinary) score (p = 0.03), and ESS Total Score (p = 0.04) than the other two clusters. Cluster A also progressed significantly in PIGD score (p = 0.04). For cognitive outcomes, cluster A deteriorated significantly in visuospatial domain (p = 0.002), while cluster C (mild subtype) deteriorated significantly in executive domain (p = 0.04). Conclusions The severe cluster had significantly faster progression, particularly in mood and perceptual NMS domains, visuospatial cognitive performances, and postural instability gait scores. Our findings will be helpful for clinicians to stratify and pre-emptively manage PD patients by developing intervention strategies to counter the progression of these domains.
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Affiliation(s)
- Xiao Deng
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Seyed Ehsan Saffari
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
- Centre for Quantitative Medicine, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Bin Xiao
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Samuel Yong Ern Ng
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
| | - Nicole Chia
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
| | - Xinyi Choi
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
| | - Dede Liana Heng
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
| | - Ebonne Ng
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
| | - Zheyu Xu
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
| | - Kay-Yaw Tay
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
| | - Wing-Lok Au
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Eng-King Tan
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Louis C.S. Tan
- Department of Neurology, National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
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49
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Pardo-Valencia J, Fernández-García C, Alonso-Frech F, Foffani G. Oscillatory vs. non-oscillatory subthalamic beta activity in Parkinson's disease. J Physiol 2024; 602:373-395. [PMID: 38084073 DOI: 10.1113/jp284768] [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: 04/02/2023] [Accepted: 11/13/2023] [Indexed: 01/16/2024] Open
Abstract
Parkinson's disease is characterized by exaggerated beta activity (13-35 Hz) in cortico-basal ganglia motor loops. Beta activity includes both periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and excitation/inhibition balance (i.e. non-oscillatory activity). However, the relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. We recorded, modelled and analysed subthalamic local field potentials in parkinsonian patients at rest while off or on medication. Autoregressive modelling with additive 1/f noise clarified the relationships between measures of beta activity in the time domain (i.e. amplitude and duration of beta bursts) or in the frequency domain (i.e. power and sharpness of the spectral peak) and oscillatory vs. non-oscillatory activity: burst duration and spectral sharpness are specifically sensitive to oscillatory activity, whereas burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Our experimental data confirmed the model predictions and assumptions. We subsequently analysed the effect of levodopa, obtaining strong-to-extreme Bayesian evidence that oscillatory beta activity is reduced in patients on vs. off medication, with moderate evidence for absence of modulation of the non-oscillatory component. Finally, specifically the oscillatory component of beta activity correlated with the rate of motor progression of the disease. Methodologically, these results provide an integrative understanding of beta-based biomarkers relevant for adaptive deep brain stimulation. Biologically, they suggest that primarily the oscillatory component of subthalamic beta activity is dopamine dependent and may play a role not only in the pathophysiology but also in the progression of Parkinson's disease. KEY POINTS: Beta activity in Parkinson's disease includes both true periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and synaptic balance (i.e. non-oscillatory activity). The relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. Burst duration and spectral sharpness are specifically sensitive to oscillatory activity, while burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Only the oscillatory component of subthalamic beta activity is dopamine-dependent. Stronger beta oscillatory activity correlates with faster motor progression of the disease.
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Affiliation(s)
- Jesús Pardo-Valencia
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | - Carla Fernández-García
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
| | - Fernando Alonso-Frech
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Department of Neurology, San Carlos Research Health Intitute (IdISSC), Hospital Clínico San Carlos, Madrid, Spain
| | - Guglielmo Foffani
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
- Hospital Nacional de Parapléjicos, SESCAM, Toledo, Spain
- Instituto de Salud Carlos III, CIBERNED, Madrid, Spain
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50
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Antoniades CA, Spering M. Eye movements in Parkinson's disease: from neurophysiological mechanisms to diagnostic tools. Trends Neurosci 2024; 47:71-83. [PMID: 38042680 DOI: 10.1016/j.tins.2023.11.001] [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: 08/04/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 12/04/2023]
Abstract
Movement disorders such as Parkinson's disease (PD) impact oculomotor function - the ability to move the eyes accurately and purposefully to serve a multitude of sensory, cognitive, and secondary motor tasks. Decades of neurophysiological research in monkeys and behavioral studies in humans have characterized the neural basis of healthy oculomotor control. This review links eye movement abnormalities in persons living with PD to the underlying neurophysiological mechanisms and pathways. Building on this foundation, we highlight recent progress in using eye movements to gauge symptom severity, assess treatment effects, and serve as potential precision biomarkers. We conclude that whereas eye movements provide insights into PD mechanisms, based on current evidence they appear to lack sufficient sensitivity and specificity to serve as a standalone diagnostic tool. Their full potential may be realized when combined with other disease indicators in big datasets.
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Affiliation(s)
- Chrystalina A Antoniades
- Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK.
| | - Miriam Spering
- Department of Ophthalmology & Visual Sciences and Djavad Mowafaghian Center for Brain Health, University of British Columbia, Vancouver, Canada.
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