Abstract
Fatigue is a common and disabling non-motor symptom (NMS) of Parkinson’s disease (PD); however, it has been poorly understood in patients with progressive supranuclear palsy (PSP). We ...investigated the association between fatigue, clinical features, and other NMS in patients with probable PSP. In 72 probable PSP patients, fatigue was investigated using the Parkinson Fatigue Scale (PFS). Further, all patients were evaluated using the PSP rating scale (PSPRS), Beck Depression Inventory (BDI), Mini-Mental State Examination (MMSE), Frontal Assessment Battery (FAB), PD Sleep Scale (PDSS), NMS scale (NMSS), PD Questionnaire-39 summary index (PDQ-39 SI), and Scale for outcomes in PD-Autonomic (SCOPA-AUT). The prevalence of fatigue assessed by PFS was 38.9% (28/72) in patients with PSP. The secondary fatigue was defined as fatigued patients with depression and/or sleep disturbances. We divided the patients into primary (n = 15), secondary (n = 13), and non-fatigue groups. There were no differences in age, sex, disease duration, and PSPRS, PDSS, MMSE, and FAB scores among the three groups. The primary fatigue group had higher scores in PDQ-39 SI compared to the non-fatigue group. The secondary fatigue group showed higher scores in NMSS, PDQ-39 SI, and SCOPA-AUT compared to the non-fatigue group. PFS was positively correlated with NMSS and PDQ-39 SI and SCOPA-AUT. Fatigue is common in patients with PSP and is associated with the NMS and the quality of life in these patients. The present study provides meaningful insight into fatigue in patients with PSP.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The age at onset (AAO) is an important determinant in Parkinson's disease (PD). Neuroimaging genetics is suitable for studying AAO in PD as it jointly analyzes imaging and genetics. We aimed to ...identify features associated with AAO in PD by applying the objective-specific neuroimaging genetics approach and constructing an AAO prediction model. Our objective-specific neuroimaging genetics extended the sparse canonical correlation analysis by an additional data type related to the target task to investigate possible associations of the imaging-genetic, genetic-target, and imaging-target pairs simultaneously. The identified imaging, genetic, and combined features were used to construct analytical models to predict the AAO in a nested five-fold cross-validation. We compared our approach with those from two feature selection approaches where only associations of imaging-target and genetic-target were explored. Using only imaging features, AAO prediction was accurate in all methods. Using only genetic features, the results from other methods were worse or unstable compared to our model. Using both imaging and genetic features, our proposed model predicted the AAO well (r = 0.5486). Our findings could have significant impacts on the characterization of prodromal PD and contribute to diagnosing PD early because genetic features could be measured accurately from birth.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Transcranial direct current stimulation (tDCS) is a non-invasive technique that has been widely studied as an alternative treatment for Parkinson's disease (PD). However, its clinical benefit remains ...unclear. In this study, we aimed to investigate the effect of tDCS on the central cholinergic system and cortical excitability in mainly akinetic rigid-type patients with PD.
In total, 18 patients with PD were prospectively enrolled and underwent 5 sessions of anodal tDCS on the M1 area, which is on the contralateral side of the dominant hand. We excluded patients with PD who had evident resting tremor of the hand to reduce the artifact of electrophysiologic findings. We compared clinical scales reflecting motor, cognitive, and mood symptoms between pre- and post-tDCS. Additionally, we investigated the changes in electrophysiologic parameters, such as short latency afferent inhibition (SAI) (%), which reflects the central cholinergic system.
The United Parkinson's Disease Rating Scale Part 3 (UPDRS-III), the Korean-Montreal Cognitive Assessment (MoCA-K), and Beck Depression Inventory (BDI) scores were significantly improved after anodal tDCS (
< 0.01,
< 0.01, and
< 0.01). Moreover, motor evoked potential amplitude ratio (MEPAR) (%) and integrated SAI showed significant improvement after tDCS (
< 0.01 and
< 0.01). The mean values of the change in integrated SAI (%) were significantly correlated with the changes in UPDRS-III scores; however, the MoCA-K and BDI scores did not show differences.
Anodal tDCS could influence the central cholinergic system, such as frontal cortical excitability and depression in PD. This mechanism could underlie the clinical benefit of tDCS in patients with PD.
Introduction. Fatigue and orthostatic hypotension (OH) are common and disabling nonmotor symptoms (NMSs) of Parkinson’s disease (PD), but none of the studies have reported on the longitudinal ...association between fatigue and OH. Methods. Drug-naïve PD patients were recruited from a hospital-based cohort and evaluated with the Parkinson Fatigue Scale (PFS), head-up tilt test, Unified PD Rating Scale, Hoehn and Yahr stage, Montreal Cognitive Assessment, Scale for Outcomes in PD-Autonomic (SCOPA-AUT), Beck Depression Inventory (BDI), Beck Anxiety Inventory, PD Sleep Scale, and medications at the baseline and follow-up visits. Results. A total of 80 patients were included, and the mean ages were 66.6 and 63.8 years in the fatigue and nonfatigue groups, respectively. The prevalence of fatigue was 17.5% (14/80) at the baseline and follow-up (mean follow-up: 23.3 ± 9.9 months). The prevalence of OH in the fatigue group was 57.1%, and it was significantly higher than that of the nonfatigue group. Six of the 14 patients (42.9%) in the fatigue group had persistent fatigue at the follow-up, and eight of them (57.1%) converted to the nonfatigue group. Logistic regression analysis demonstrated that the changes of BDI and the presence of OH at the baseline were the predictors for fatigue in drug-naïve PD. Conclusion. Fatigue is a common NMS in PD but can vary depending on the disease course. OH and depression are the most relevant predictors for the development of fatigue in drug-naïve PD. The present study suggests that the management of autonomic symptoms and depression might be helpful for managing fatigue in PD.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Falls are a common and disabling symptom in patients with Parkinson's disease (PD). For prevention, it is important to understand the pathophysiology of falls in PD patients, but the predictors for ...the possible mechanisms underlying such falls have not been clearly elucidated. In this prospective observational study, we investigated the implications of falling direction to predict the mechanisms of recurrent falls in PD patients. We enrolled 62 recurrent fallers with PD and divided them into two groups according to the main falling directions: 45 PD fallers who fell forward (forward fallers), and 17 PD fallers who fell in the other directions (non-forward fallers). Although there was no difference in demographic data, parkinsonism, or frontal lobe function, forward fallers showed more severe falls and tended to fall during walking or turning, while non-forward fallers usually fell during sitting/standing or turning. Additionally, forward fallers revealed higher score on a freezing of gait (FOG) questionnaire. Logistic regression analysis demonstrated that FOG was associated with falling forward, while balance impairment, akinetic-rigid subtype, and neuropsychiatric symptoms were associated with falling into the other directions. Our results indicate that FOG and balance impairment are two major mechanisms for recurrent falling in PD patients, and falling direction is an important predictor for these mechanisms.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Objectives:
We aimed to validate the accuracy of blood pressure (BP) measurement using a smartwatch in patients with Parkinson's disease (PD).
Materials and Methods:
We compared 168 pairs of BP (
n
= ...56) measurements acquired by a smartwatch (SM-R850) with those measured by a sphygmomanometer (reference device).
Results:
Differences between the smartwatch BP and reference BP measurements were compared. The mean and standard deviation of the differences systolic BP (SBP) and diastolic BP (DBP), measured by smartwatch and reference device, fulfilled both criterion 1 (0.4 ± 4.6 and 1.1 ± 4.5 mm Hg for DBP and SBP, respectively) and criterion 2 (0.2 ± 2.5 and 0.9 ± 2.4 mm Hg for DBP and SBP, respectively) of the BP validation criterion of the International Organization for Standardization.
Conclusion:
BP measurement using a smartwatch with a photoplethysmography sensor is an accurate and reliable method in patients with PD.
Subjective cognitive complaints (SCC) refer to self-reported cognitive decline that may or may not be reflected in objective neuropsychological evaluations. Such SCC are prevalent in ...neurodegenerative diseases, including Parkinson's disease (PD), but the prevalence and clinical features in patients with progressive supranuclear palsy (PSP) have not been investigated.
We recruited 83 PSP patients without dementia and investigated their SCC using a semi-structured interview. Comprehensive neuropsychological test results and patient clinical features were compared according to presence of SCC and underlying cognitive state.
Among the 83 patients, 16 had normal cognition (NC), 67 had mild cognitive impairment (MCI), and 36 (43.4%) reported SCC. Among NC patients, 37.5% (6/16) had SCC, while 44.8% (30/67) of MCI patients reported SCC. There were no differences between the neuropsychological test results or demographic and clinical characteristics of PSP patients with or without SCC in the NC group. The demographic and clinical characteristics of the MCI+SCC (MCI with SCC)and MCI-SCC (MCI without SCC) groups were comparable, but the MCI+SCC group had significantly worse neuropsychological scores than the MCI-SCC group, particularly in tests assessing attention, language, visual memory, and fronto-executive function domains.
While SCC are commonly reported by PSP patients, patients with PSP and MCI+SCC had worse cognitive function than those who did not report SCC. These findings suggest that SCC in PSP patients with MCI could be a worsening sign of cognitive function. Therefore, it is crucial for physicians to assess SCC in PSP patients and to provide timely diagnosis and management of cognitive decline.
This study aimed to investigate the association between nonarteritic anterior ischemic optic neuropathy (NAION) and Parkinson's disease (PD) using a retrospective, nationwide, population-based cohort ...in South Korea. This study utilized data from the Korean National Health Insurance database, including 43,960 NAION patients and 219,800 age- and sex-matched controls. Cox proportional hazards regression models were used to assess the risk of developing PD in the NAION group compared to the control group after adjusting for various confounding factors. Subgroup analyses were conducted based on sex, age, and comorbidities. The incidence rate of PD was higher in the NAION group (1.326 per 1000 person-years) than in the control group (0.859 per 1000 person-years). After adjusting for confounding factors, the risk of developing PD was significantly higher in the NAION group (adjusted hazard ratio aHR 1.516, 95% confidence interval CI 1.300-1.769). Subgroup analyses did not reveal a significant difference in the risk of PD development based on sex, age, or comorbidities. This retrospective, nationwide, population-based cohort study revealed a significant association between NAION and an increased risk of developing PD in a South Korean population. The incidence rate of PD was observed to be higher in individuals diagnosed with NAION than in age- and sex-matched controls even after adjusting for potential confounding variables, with the risk being approximately 51.6% higher in the NAION group. Further research is necessary to elucidate the underlying pathophysiological mechanisms linking NAION to PD and to determine whether similar associations exist in other ethnic and geographical populations.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance ...the diagnostic performance, we adopt deep learning (DL) models in brain MRI segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls (Formula: see text) and patients with PD (Formula: see text), multiple systemic atrophy (Formula: see text), and progressive supranuclear palsy (Formula: see text) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard non-DL model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotated data for DL models, the representative convolutional neural network (CNN) and vision transformer (ViT)-based models. Dice scores and the area under the curve (AUC) for differentiating normal, PD, and P-plus cases were calculated to determine the measure to which FS performance can be reproduced as-is while increasing speed by the DL approaches. The segmentation times of CNN and ViT for the six brain structures per patient were 51.26 ± 2.50 and 1101.82 ± 22.31 s, respectively, being 14 to 300 times faster than FS (15,735 ± 1.07 s). Dice scores of both DL models were sufficiently high (> 0.85) so their AUCs for disease classification were not inferior to that of FS. For classification of normal vs. P-plus and PD vs. P-plus (except multiple systemic atrophy - Parkinsonian type) based on all brain parts, the DL models and FS showed AUCs above 0.8, demonstrating the clinical value of DL models in addition to FS. DL significantly reduces the analysis time without compromising the performance of brain segmentation and differential diagnosis. Our findings may contribute to the adoption of DL brain MRI segmentation in clinical settings and advance brain research.
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