Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder and is characterized by progressive dopaminergic and nondopaminergic neuronal loss and the presence of Lewy bodies, which are primarily composed of aggregated α-synuclein. Despite advancements in symptomatic therapies, such as dopamine replacement and deep brain stimulation, no disease-modifying therapies (DMTs) have been identified to slow or arrest neurodegeneration in patients with PD. Challenges in DMT development include disease heterogeneity, the absence of reliable biomarkers, and the multifaceted pathophysiology of PD, encompassing neuroinflammation, mitochondrial dysfunction, lysosomal impairment, and oxidative stress. Drug repositioning and repurposing strategies using existing drugs for new therapeutic applications offer promising approaches to accelerate the development of DMTs for PD. These strategies minimize time, cost, and risk by using compounds with established safety profiles. Prominent candidates include glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors, ambroxol, calcium channel blockers, statins, iron-chelating agents, c-Abl inhibitors, and memantine. Although preclinical and early clinical studies have demonstrated encouraging results, numerous phase III trials have yielded unfavorable outcomes, elucidating the complexity of PD pathophysiology and the need for innovative trial designs. This review evaluates the potential of prioritized repurposed drugs for PD, focusing on their mechanisms, preclinical evidence, and clinical trial outcomes, and highlights the ongoing challenges and opportunities in this field.

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder and is characterized by progressive dopaminergic and nondopaminergic neuronal loss and the presence of Lewy bodies, which are primarily composed of aggregated α-synuclein. Despite advancements in symptomatic therapies, such as dopamine replacement and deep brain stimulation, no disease-modifying therapies (DMTs) have been identified to slow or arrest neurodegeneration in patients with PD. Challenges in DMT development include disease heterogeneity, the absence of reliable biomarkers, and the multifaceted pathophysiology of PD, encompassing neuroinflammation, mitochondrial dysfunction, lysosomal impairment, and oxidative stress. Drug repositioning and repurposing strategies using existing drugs for new therapeutic applications offer promising approaches to accelerate the development of DMTs for PD. These strategies minimize time, cost, and risk by using compounds with established safety profiles. Prominent candidates include glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors, ambroxol, calcium channel blockers, statins, iron-chelating agents, c-Abl inhibitors, and memantine. Although preclinical and early clinical studies have demonstrated encouraging results, numerous phase III trials have yielded unfavorable outcomes, elucidating the complexity of PD pathophysiology and the need for innovative trial designs. This review evaluates the potential of prioritized repurposed drugs for PD, focusing on their mechanisms, preclinical evidence, and clinical trial outcomes, and highlights the ongoing challenges and opportunities in this field.

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder and is characterized by progressive dopaminergic and nondopaminergic neuronal loss and the presence of Lewy bodies, which are primarily composed of aggregated α-synuclein. Despite advancements in symptomatic therapies, such as dopamine replacement and deep brain stimulation, no disease-modifying therapies (DMTs) have been identified to slow or arrest neurodegeneration in patients with PD. Challenges in DMT development include disease heterogeneity, the absence of reliable biomarkers, and the multifaceted pathophysiology of PD, encompassing neuroinflammation, mitochondrial dysfunction, lysosomal impairment, and oxidative stress. Drug repositioning and repurposing strategies using existing drugs for new therapeutic applications offer promising approaches to accelerate the development of DMTs for PD. These strategies minimize time, cost, and risk by using compounds with established safety profiles. Prominent candidates include glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors, ambroxol, calcium channel blockers, statins, iron-chelating agents, c-Abl inhibitors, and memantine. Although preclinical and early clinical studies have demonstrated encouraging results, numerous phase III trials have yielded unfavorable outcomes, elucidating the complexity of PD pathophysiology and the need for innovative trial designs. This review evaluates the potential of prioritized repurposed drugs for PD, focusing on their mechanisms, preclinical evidence, and clinical trial outcomes, and highlights the ongoing challenges and opportunities in this field.

Objective: The Montreal Cognitive Assessment (MoCA) is recommended for general cognitive evaluation in Parkinson’s disease (PD) patients. However, age- and education-adjusted cutoffs specifically for PD have not been developed or systematically validated across PD cohorts with diverse education levels. Methods: In this retrospective analysis, we utilized data from 1,293 Korean patients with PD whose cognitive diagnoses were determined through comprehensive neuropsychological assessments. Age- and education-adjusted cutoffs were formulated based on 1,202 patients with PD. To identify the optimal machine learning model, clinical parameters and MoCA domain scores from 416 patients with PD were used. Comparative analyses between machine learning methods and different cutoff criteria were conducted on an additional 91 consecutive patients with PD. Results: The cutoffs for cognitive impairment decrease with increasing age within the same education level. Similarly, lower education levels within the same age group correspond to lower cutoffs. For individuals aged 60–80 years, cutoffs were set as follows: 25 or 24 years for those with more than 12 years of education, 23 or 22 years for 10–12 years, and 21 or 20 years for 7–9 years. Comparisons between age- and education-adjusted cutoffs and the machine learning method showed comparable accuracies. The cutoff method resulted in a higher sensitivity (0.8627), whereas machine learning yielded higher specificity (0.8250). Conclusion Both the age- and education-adjusted cutoff methods and machine learning methods demonstrated high effectiveness in detecting cognitive impairment in PD patients. This study highlights the necessity of tailored cutoffs and suggests the potential of machine learning to improve cognitive assessment in PD patients.

Objective: The Scales for Outcomes in Parkinson’s Disease–Cognition (SCOPA-Cog) was developed to assess cognition in patients with Parkinson’s disease (PD). In this study, we aimed to evaluate the validity and reliability of the Korean version of the SCOPACog (K-SCOPA-Cog). Methods: We enrolled 129 PD patients with movement disorders from 31 clinics in South Korea. The original version of the SCOPA-Cog was translated into Korean using the translation-retranslation method. The test–retest method with an intraclass correlation coefficient (ICC) and Cronbach’s alpha coefficient were used to assess reliability. Spearman’s rank correlation analysis with the Montreal Cognitive Assessment-Korean version (MOCA-K) and the Korean Mini-Mental State Examination (K-MMSE) were used to assess concurrent validity. Results: The Cronbach’s alpha coefficient was 0.797, and the ICC was 0.887. Spearman’s rank correlation analysis revealed a significant correlation with the K-MMSE and MOCA-K scores (r = 0.546 and r = 0.683, respectively). Conclusion Our results demonstrate that the K-SCOPA-Cog has good reliability and validity.

Background: and PurposeWe aimed to determine the effect of demographic factors on cortical thickness and brain glucose metabolism in healthy aging subjects. Methods: The following tests were performed on 71 subjects with normal cognition: neurological examination, 3-tesla magnetic resonance imaging, 18F-fluorodeoxyglucose positron-emission tomography, and neuropsychological tests. Cortical thickness and brain metabolism were measured using vertex- and voxelwise analyses, respectively. General linear models (GLMs) were used to determine the effects of age, sex, and education on cortical thickness and brain glucose metabolism. The effects of mean lobar cortical thickness and mean lobar metabolism on neuropsychological test scores were evaluated using GLMs after controlling for age, sex, and education. The intracranial volume (ICV) was further included as a predictor or covariate for the cortical thickness analyses. Results: Age was negatively correlated with the mean cortical thickness in all lobes (frontal and parietal lobes, p=0.001; temporal and occipital lobes, p<0.001) and with the mean temporal metabolism (p=0.005). Education was not associated with cortical thickness or brain metabolism in any lobe. Male subjects had a lower mean parietal metabolism than did female subjects (p<0.001), while their mean cortical thicknesses were comparable. ICV was positively correlated with mean cortical thickness in the frontal (p=0.016), temporal (p=0.009), and occipital (p=0.007) lobes. The mean lobar cortical thickness was not associated with cognition scores, while the mean temporal metabolism was positively correlated with verbal memory test scores. Conclusions Age and sex affect cortical thickness and brain glucose metabolism in different ways. Demographic factors must therefore be considered in analyses of cortical thickness and brain metabolism.

Subjective cognitive complaints (SCCs) refer to self-perceived cognitive decline and are related to objective cognitive decline. SCCs in cognitively normal individuals are considered a preclinical sign of subsequent cognitive impairment due to Alzheimer’s disease, and SCCs in cognitively normal patients with Parkinson’s disease (PD) are also gaining attention. The aim of this review was to provide an overview of the current research on SCCs in cognitively normal patients with PD. A systematic search found a lack of consistency in the methodologies used to define and measure SCCs. Although the association between SCCs and objective cognitive performance in cognitively normal patients with PD is controversial, SCCs appear to be predictive of subsequent cognitive decline. These findings support the clinical value of SCCs in cognitively normal status in PD; however, further convincing evidence from biomarker studies is needed to provide a pathophysiological basis for these findings. Additionally, a consensus on the definition and assessment of SCCs is needed for further investigations.

Background: and Purpose To determine the imaging characteristics and cutoff value of18F-florapronol (FC119S) quantitative analysis for detecting β-amyloid positivity and Al- zheimer’s disease (AD), we compared the findings of FC119S and 18F-florbetaben (FBB) positron-emission tomography (PET) in patients with cognitive impairment. Methods: We prospectively enrolled 35 patients with cognitive impairment who underwent FBB-PET, FC119S-PET, and brain magnetic resonance imaging. We measured global and vertex-wise standardized uptake value ratios (SUVRs) using a surface-based method with the cerebellar gray matter as reference. Optimal global FC119S SUVR cutoffs were determined using receiver operating characteristic curves for β-amyloid positivity based on the global FBB SUVR of 1.478 and presence of AD, respectively. We evaluated the global and vertex-wise SUVR correlations between the two tracers. In addition, we performed correlation analysis for global or vertex-wise SUVR of each tracer with the vertex-wise cortical thicknesses. Results: The optimal global FC119S SUVR cutoff value was 1.385 both for detecting β-amyloid positivity and for detecting AD. Based on the global SUVR cutoff value of each tracer, 32 (91.4%) patients had concordant β-amyloid positivity. The SUVRs of FC119S and FBB had strong global (r=0.72) and vertex-wise (r>0.7) correlations in the overall cortices, except for the parietal and temporal cortices (0.4Conclusions Quantitative FC119S-PET analysis provided reliable information for detecting β-amyloid deposition and the presence of AD.

Objective: To investigate whether there is a link between cognitive function and motor reserve (i.e., individual capacity to cope with nigrostriatal dopamine depletion) in patients with newly diagnosed Parkinson’s disease (PD). Methods: A total of 163 patients with drug-naïve PD who underwent 18F-FP-CIT PET, brain MRI, and a detailed neuropsychological test were enrolled. We estimated individual motor reserve based on initial motor deficits and striatal dopamine depletion using a residual model. We performed correlation analyses between motor reserve estimates and cognitive composite scores. Diffusion connectometry analysis was performed to map the white matter fiber tracts, of which fractional anisotropy (FA) values were well correlated with motor reserve estimates. Additionally, Cox regression analysis was used to assess the effect of initial motor reserve on the risk of dementia conversion. Results: The motor reserve estimate was positively correlated with the composite score of the verbal memory function domain (γ = 0.246) and with the years of education (γ = 0.251). Connectometry analysis showed that FA values in the left fornix were positively correlated with the motor reserve estimate, while no fiber tracts were negatively correlated with the motor reserve estimate. Cox regression analysis demonstrated that higher motor reserve estimates tended to be associated with a lower risk of dementia conversion (hazard ratio, 0.781; 95% confidence interval, 0.576–1.058). Conclusion The present study demonstrated that the motor reserve estimate was well correlated with verbal memory function and with white matter integrity in the left fornix, suggesting a possible link between cognition and motor reserve in patients with PD.