Clinical Characteristics and Relevance of Dizziness in Patients with de novo Parkinson's Disease.

BACKGROUND: The purpose of this study was to determine the detailed characteristics of dizziness in patients with de novo Parkinson's disease (PD) and the clinical implications of dizziness. METHODS: Ninety-three people with de novo PD were enrolled between July 2017 and August 2022 for this retrospective study. Using each representative scale, various motor and non-motor symptoms were assessed. In addition, clinical manifestations of dizziness in those patients, including its presence, type, frequency, and duration of occurrence, were investigated. RESULTS: Thirty-nine patients with de novo PD reported dizziness, with presyncope being the most common (38%). The most common frequency was several times a week (51%). The most common duration was a few seconds (67%). Multivariable logistic regression analysis showed that dizziness was more common in women than in men {odds ratio (OR): 3.3601, 95% confidence interval (CI): 1.0820-10.4351, p = 0.0361}. Dizziness was significantly related to non-motor symptoms of low global cognition (OR: 0.8372, 95% CI: 0.7285-0.9622, p = 0.0123) and severe autonomic dysfunction (OR: 1.1112, 95% CI: 1.0297-1.1991, p = 0.0067). A post-hoc analysis revealed that dizziness was only associated with cardiovascular dysautonomia (adjusted OR: 10.2377, 95% CI: 3.3053-31.7098, p < 0.0001) among several domains of dysautonomia. CONCLUSIONS: About 42% of patients with de novo PD complained of dizziness. The occurrence of dizziness in those people was highly associated with female gender women, cognitive impairment, and cardiovascular dysautonomia. These results suggest that clinicians should pay close attention when patients with PD complain of dizziness.

Characteristics of discordance between amyloid positron emission tomography and plasma amyloid-ß 42/40 positivity.

Various plasma biomarkers for amyloid-ß (Aß) have shown high predictability of amyloid PET positivity. However, the characteristics of discordance between amyloid PET and plasma Aß42/40 positivity are poorly understood. Thorough interpretation of discordant cases is vital as Aß plasma biomarker is imminent to integrate into clinical guidelines. We aimed to determine the characteristics of discordant groups between amyloid PET and plasma Aß42/40 positivity, and inter-assays variability depending on plasma assays. We compared tau burden measured by PET, brain volume assessed by MRI, cross-sectional cognitive function, longitudinal cognitive decline and polygenic risk score (PRS) between PET/plasma groups (PET-/plasma-, PET-/plasma+, PET+/plasma-, PET+/plasma+) using Alzheimer's Disease Neuroimaging Initiative database. Additionally, we investigated inter-assays variability between immunoprecipitation followed by mass spectrometry method developed at Washington University (IP-MS-WashU) and Elecsys immunoassay from Roche (IA-Elc). PET+/plasma+ was significantly associated with higher tau burden assessed by PET in entorhinal, Braak III/IV, and Braak V/VI regions, and with decreased volume of hippocampal and precuneus regions compared to PET-/plasma-. PET+/plasma+ showed poor performances in global cognition, memory, executive and daily-life function, and rapid cognitive decline. PET+/plasma+ was related to high PRS. The PET-/plasma+ showed intermediate changes between PET-/plasma- and PET+/plasma+ in terms of tau burden, hippocampal and precuneus volume, cross-sectional and longitudinal cognition, and PRS. PET+/plasma- represented heterogeneous characteristics with most prominent variability depending on plasma assays. Moreover, IP-MS-WashU showed more linear association between amyloid PET standardized uptake value ratio and plasma Aß42/40 than IA-Elc. IA-Elc showed more plasma Aß42/40 positivity in the amyloid PET-negative stage than IP-MS-WashU. Characteristics of PET-/plasma+ support plasma biomarkers as early biomarker of amyloidopathy prior to amyloid PET. Various plasma biomarker assays might be applied distinctively to detect different target subjects or disease stages.

Association Between Baseline Gait Parameters and Future Fall Risk in Patients With De Novo Parkinson's Disease: Forward Versus Backward Gait.

BACKGROUND AND PURPOSE: Falls are not uncommon even in patients with early stages of Parkinson's disease (PD). The aims of this study were to determine the relationships between gait parameters and falls and identify crucial gait parameters for predicting future falls in patients with de novo PD. METHODS: We prospectively recruited patients with de novo PD, and evaluated their baseline demographics, global cognitive function on the Montreal Cognitive Assessment test, and parkinsonian motor symptoms including their subtypes. Both forward gait (FG) and backward gait (BG) were measured using the GAITRite system. The history of falls in consecutive patients with de novo PD was examined along with 1 year of follow-up data. RESULTS: Among the 76 patients with de novo PD finally included in the study, 16 (21.1%) were classified as fallers. Fallers had slower gait and shorter stride for FG and BG parameters than did non-fallers, while stride-time variability was greater in fallers but only for BG. Multivariable logistic regression analysis revealed that slow gait was an independent risk factor in BG. CONCLUSIONS: Among the patients with de novo PD, gait speed and stride length were more impaired for both FG and BG in fallers than in non-fallers. It was particularly notable that slow BG was significantly associated with future fall risk, indicating that BG speed is a potential biomarker for predicting future falls in patients with early-stage PD.

Association of Dizziness-Related Handicap or Disability with Clinical Features in Patients with Early Parkinson's Disease.

BACKGROUND: Complaining of dizziness is common in patients with Parkinson's disease (PD) even at the early phase of the disease. Therefore, regarding motor or non-motor symptoms, clinical implication of subjective dizziness in early Parkinsonian patients is needed to be explored. METHODS: Eighty patients diagnosed with early PD (defined by disease duration of five years or less) were retrospectively enrolled for the study. Dizziness handicap inventory (DHI), the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) Functional Level Scale (FLS), and clinical features of parkinsonian motor and non-motor symptoms using representative measurements. RESULTS: Through simple and multiple linear regression analyses, we found that both DHI and FLS were significantly and positively correlated with postural instability/gait disorder (PIGD) score but negatively with the Montreal cognitive assessment (MoCA) score. CONCLUSIONS: We found that subjective dizziness in patients with early PD was related to not only axial symptoms of PIGD, but also global cognitive function of MoCA. Further research is required to confirm our results.

Up-Sampling Method for Low-Resolution LiDAR Point Cloud to Enhance 3D Object Detection in an Autonomous Driving Environment.

Automobile datasets for 3D object detection are typically obtained using expensive high-resolution rotating LiDAR with 64 or more channels (Chs). However, the research budget may be limited such that only a low-resolution LiDAR of 32-Ch or lower can be used. The lower the resolution of the point cloud, the lower the detection accuracy. This study proposes a simple and effective method to up-sample low-resolution point cloud input that enhances the 3D object detection output by reconstructing objects in the sparse point cloud data to produce more dense data. First, the 3D point cloud dataset is converted into a 2D range image with four channels: x, y, z, and intensity. The interpolation on the empty space is calculated based on both the pixel distance and range values of six neighbor points to conserve the shapes of the original object during the reconstruction process. This method solves the over-smoothing problem faced by the conventional interpolation methods, and improves the operational speed and object detection performance when compared to the recent deep-learning-based super-resolution methods. Furthermore, the effectiveness of the up-sampling method on the 3D detection was validated by applying it to baseline 32-Ch point cloud data, which were then selected as the input to a point-pillar detection model. The 3D object detection result on the KITTI dataset demonstrates that the proposed method could increase the mAP (mean average precision) of pedestrians, cyclists, and cars by 9.2%p, 6.3%p, and 5.9%p, respectively, when compared to the baseline of the low-resolution 32-Ch LiDAR input. In future works, various dataset environments apart from autonomous driving will be analyzed.