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DeRosier F, Hibbs C, Alessi K, Padda I, Rodriguez J, Pradeep S, Parmar MS. Progressive supranuclear palsy: Neuropathology, clinical presentation, diagnostic challenges, management, and emerging therapies. Dis Mon 2024; 70:101753. [PMID: 38908985 DOI: 10.1016/j.disamonth.2024.101753] [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] [Indexed: 06/24/2024]
Abstract
Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized by the accumulation of 4R-tau protein aggregates in various brain regions. PSP leads to neuronal loss, gliosis, and tau-positive inclusions, such as neurofibrillary tangles, tufted astrocytes, and coiled bodies. These pathological changes mainly affect the brainstem and the basal ganglia, resulting in distinctive MRI features, such as the hummingbird and morning glory signs. PSP shows clinical heterogeneity and presents as different phenotypes, the most classical of which is Richardson's syndrome (PSP-RS). The region of involvement and the mode of atrophy spread can further distinguish subtypes of PSP. PSP patients can experience various signs and symptoms, such as postural instability, supranuclear ophthalmoplegia, low amplitude fast finger tapping, and irregular sleep patterns. The most common symptoms of PSP are postural instability, falls, vertical gaze palsy, bradykinesia, and cognitive impairment. These features often overlap with those of Parkinson's disease (PD) and other Parkinsonian syndromes, making the diagnosis challenging. PSP is an essential clinical topic to research because it is a devastating and incurable disease. However, there are still many gaps in knowledge about its pathophysiology, diagnosis, and treatment. Several clinical trials are underway to test noveltherapies that target tau in various ways, such as modulating its post-translational modifications, stabilizing its interaction with microtubules, or enhancing its clearance by immunotherapy. These approaches may offer new hope for slowing down the progression of PSP. In this review, we aim to provide an overview of the current knowledge on PSP, from its pathogenesis to its management. We also discuss the latest advances and future directions in PSP research.
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Affiliation(s)
- Frederick DeRosier
- Department of Foundational Sciences, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Clearwater, FL, United States of America
| | - Cody Hibbs
- Department of Foundational Sciences, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Clearwater, FL, United States of America
| | - Kaitlyn Alessi
- Department of Family Medicine, University of Florida, Gainesville, United States of America
| | - Inderbir Padda
- Department of Internal Medicine, Richmond University Medical Center, Staten Island, New York, United States of America
| | - Jeanette Rodriguez
- Department of Family Medicine, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Clearwater, Florida, United States of America
| | - Swati Pradeep
- Department of Movement Disorders, UTHealth Houston Neurosciences Neurology - Texas Medical Center, Texas, United States of America
| | - Mayur S Parmar
- Department of Foundational Sciences, Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Clearwater, FL, United States of America.
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Tsai CY, Liu WT, Hsu WH, Majumdar A, Stettler M, Lee KY, Cheng WH, Wu D, Lee HC, Kuan YC, Wu CJ, Lin YC, Ho SC. Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events. Digit Health 2023; 9:20552076231152751. [PMID: 36896329 PMCID: PMC9989412 DOI: 10.1177/20552076231152751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 01/04/2023] [Indexed: 03/08/2023] Open
Abstract
Objectives Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.
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Affiliation(s)
- Cheng-Yu Tsai
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Wen-Te Liu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wen-Hua Hsu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Arnab Majumdar
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Marc Stettler
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Kang-Yun Lee
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wun-Hao Cheng
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Dean Wu
- Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan.,Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yi-Chun Kuan
- Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan.,Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Cheng-Jung Wu
- Department of Otolaryngology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Yi-Chih Lin
- Department of Otolaryngology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Shu-Chuan Ho
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
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Magnetic Resonance Planimetry in the Differential Diagnosis between Parkinson’s Disease and Progressive Supranuclear Palsy. Brain Sci 2022; 12:brainsci12070949. [PMID: 35884755 PMCID: PMC9313181 DOI: 10.3390/brainsci12070949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Revised: 07/12/2022] [Accepted: 07/14/2022] [Indexed: 12/10/2022] Open
Abstract
The clinical differential diagnosis between Parkinson’s disease (PD) and progressive supranuclear palsy (PSP) is often challenging. The description of milder PSP phenotypes strongly resembling PD, such as PSP-Parkinsonism, further increased the diagnostic challenge and the need for reliable neuroimaging biomarkers to enhance the diagnostic certainty. This review aims to summarize the contribution of a relatively simple and widely available imaging technique such as MR planimetry in the differential diagnosis between PD and PSP, focusing on the recent advancements in this field. The development of accurate MR planimetric biomarkers, together with the implementation of automated algorithms, led to robust and objective measures for the differential diagnosis of PSP and PD at the individual level. Evidence from longitudinal studies also suggests a role of MR planimetry in predicting the development of the PSP clinical signs, allowing to identify PSP patients before they meet diagnostic criteria when their clinical phenotype can be indistinguishable from PD. Finally, promising evidence exists on the possible association between MR planimetric measures and the underlying pathology, with important implications for trials with new disease-modifying target therapies.
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Mazzucchi S, Del Prete E, Costagli M, Frosini D, Paoli D, Migaleddu G, Cecchi P, Donatelli G, Morganti R, Siciliano G, Cosottini M, Ceravolo R. Morphometric imaging and quantitative susceptibility mapping as complementary tools in the diagnosis of parkinsonisms. Eur J Neurol 2022; 29:2944-2955. [PMID: 35700041 PMCID: PMC9545010 DOI: 10.1111/ene.15447] [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: 03/22/2022] [Revised: 06/02/2022] [Accepted: 06/09/2022] [Indexed: 11/26/2022]
Abstract
Background and purpose In the quest for in vivo diagnostic biomarkers to discriminate Parkinson's disease (PD) from progressive supranuclear palsy (PSP) and multiple system atrophy (MSA, mainly p phenotype), many advanced magnetic resonance imaging (MRI) techniques have been studied. Morphometric indices, such as the Magnetic Resonance Parkinsonism Index (MRPI), demonstrated high diagnostic value in the comparison between PD and PSP. The potential of quantitative susceptibility mapping (QSM) was hypothesized, as increased magnetic susceptibility (Δχ) was reported in the red nucleus (RN) and medial part of the substantia nigra (SNImed) of PSP patients and in the putamen of MSA patients. However, disease‐specific susceptibility values for relevant regions of interest are yet to be identified. The aims of the study were to evaluate the diagnostic potential of a multimodal MRI protocol combining morphometric and QSM imaging in patients with determined parkinsonisms and to explore its value in a population of undetermined cases. Method Patients with suspected degenerative parkinsonism underwent clinical evaluation, 3 T brain MRI and clinical follow‐up. The MRPI was manually calculated on T1‐weighted images. QSM maps were generated from 3D multi‐echo T2*‐weighted sequences. Results In determined cases the morphometric evaluation confirmed optimal diagnostic accuracy in the comparison between PD and PSP but failed to discriminate PD from MSA‐p. Significant nigral and extranigral differences were found with QSM. RN Δχ showed excellent diagnostic accuracy in the comparison between PD and PSP and good accuracy in the comparison of PD and MSA‐p. Optimal susceptibility cut‐off values of RN and SNImed were tested in undetermined cases in addition to MRPI. Conclusions A combined use of morphometric imaging and QSM could improve the diagnostic phase of degenerative parkinsonisms.
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Affiliation(s)
- Sonia Mazzucchi
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Eleonora Del Prete
- Neurology Unit, Department of Medical Specialties, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Sciences (DINOGMI), University of Genoa, Genoa, Italy.,Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Daniela Frosini
- Neurology Unit, Department of Medical Specialties, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Davide Paoli
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | | | - Paolo Cecchi
- Neuroradiology Unit, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - Graziella Donatelli
- Neuroradiology Unit, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy.,Imago7 Research Foundation, Pisa, Italy
| | | | - Gabriele Siciliano
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Mirco Cosottini
- Imago7 Research Foundation, Pisa, Italy.,Neuroradiology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Roberto Ceravolo
- Neurology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.,Centre for Neurodegenerative Diseases, Parkinson's Disease and Movement Disorders, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
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