Clinical variables and biomarkers in prediction of cognitive impairment in patients with newly diagnosed Parkinson's disease: a cohort study

Prediction of Cognitive Heterogeneity in Parkinson's Disease: A 4-Year Longitudinal Study Using Clinical, Neuroimaging, Biological and Electrophysiological Biomarkers

OBJECTIVE

Cognitive impairment in Parkinson's disease (PD) can show a very heterogeneous trajectory among patients. Here, we explored the mechanisms involved in the expression and prediction of different cognitive phenotypes over 4 years.

METHODS

In 2 independent cohorts (total n = 475), we performed a cluster analysis to identify trajectories of cognitive progression. Baseline and longitudinal level II neuropsychological assessments were conducted, and baseline structural magnetic resonance imaging, resting electroencephalogram and neurofilament light chain plasma quantification were carried out. Linear mixed-effects models were used to study longitudinal changes. Risk of mild cognitive impairment and dementia were estimated using multivariable hazard regression. Spectral power density from the electroencephalogram at baseline and source localization were computed.

RESULTS

Two cognitive trajectories were identified. Cluster 1 presented stability (PD-Stable) over time, whereas cluster 2 showed progressive cognitive decline (PD-Progressors). The PD-Progressors group showed an increased risk for evolving to PD mild cognitive impairment (HR 2.09; 95% CI 1.11-3.95) and a marked risk for dementia (HR 4.87; 95% CI 1.34-17.76), associated with progressive worsening in posterior-cortical-dependent cognitive processes. Both clusters showed equivalent clinical and sociodemographic characteristics, structural magnetic resonance imaging, and neurofilament light chain levels at baseline. Conversely, the PD-Progressors group showed a fronto-temporo-occipital and parietal slow-wave power density increase, that was in turn related to worsening at 2 and 4 years of follow-up in different cognitive measures.

INTERPRETATION

In the absence of differences in baseline cognitive function and typical markers of neurodegeneration, the further development of an aggressive cognitive decline in PD is associated with increased slow-wave power density and with a different profile of worsening in several posterior-cortical-dependent tasks. ANN NEUROL 2024;96:981-993.

Temporal ordering of cognitive impairment in Parkinson's disease patients based on disease progression models

INTRODUCTION

Identifying Parkinson's disease (PD) patients at risk of cognitive decline is crucial for enhancing clinical interventions. While several models predicting cognitive decline in PD exist, a new machine learning framework called disease progression models (DPMs) offers a data-driven approach to understand disease evolution.

METHODS

We enrolled 423 PD patients and 196 healthy controls from the Parkinson's Progression Markers Initiative (PPMI). Our study encompassed a range of biomarkers, including motor, neurocognitive, and neuroimaging evaluations at baseline and annually. A methodology was employed to select optimal combinations of biomarkers for constructing DPMs with superior predictive capabilities for both diagnosing and estimating conversion times toward cognitive decline.

RESULTS

At baseline, the approach showed excellent performance in identifying individuals at high risk of cognitive decline within the first five years. Furthermore, the proposed timeline from cognitive impairment to dementia was also used to explore clinical events such as the onset of cognitive impairment, the development of dementia or amyloid pathology. The presence of amyloid pathology did not alter the progression of cognitive impairment among PD patients.

CONCLUSIONS

Neuropsychological measures and certain biomarkers, including cerebrospinal fluid (CSF) amyloid beta 42 (Aβ42) and dopamine transporter deficits, can be used to predict cognitive decline and estimate a timeline from cognitive impairment to dementia, with amyloid pathology preceding the onset of dementia in many cases. Our DPMs suggested that the profiles of CSF Aβ42 and phosphorylated tau in PD patients may differ from those in aging patients and those with Alzheimer's disease.

Association of severe hyposmia and frontal lobe dysfunction in patients with Parkinson's disease

BACKGROUNDS AND OBJECTIVES

Severe hyposmia (SH) is a prodromal symptom of dementia associated with Parkinson's disease (PD) caused by Lewy bodies deposited in the limbic regions that connect the frontal and temporal lobes. We aimed to clarify the association between hyposmia and frontal lobe dysfunction (FLD) among patients with PD.

METHODS

Patients with PD and Hoehn & Yahr stage 1-3 at on-periods without apparent dementia were screened. FLD was defined as a score of ≤14 on the Frontal Assessment Battery (FAB). SH was defined as an average recognition threshold >4 in the T&T Olfactometer. For each subscore, a recognition score of ≥4 was defined as SH. We examined whether SH and its subscores were associated with FLD and evaluated which FAB subscore might be lower in PD patients with SH using Poisson regression analysis with a robust variance estimator.

RESULTS

We included 189 patients (median age, 68 years; 107 [57 %] male). FLD was observed in 53 (28 %) patients. Multivariable analysis showed that SH (PR 1.789, 95 % confidence intervals (CI) 1.115-2.872, p = 0.016) was associated with FLD. Regarding odor domains, only SH for fruity smells was associated with FLD (PR 1.970, 95 % CI 1.306-2.972, p = 0.001). Patients with SH had a higher subscore only for FAB-1 (similarity [conceptualization], p = 0.030), indicating linguistically mediated executive dysfunction.

CONCLUSION

In patients with PD, SH is associated with FLD, especially with linguistically mediated executive dysfunction. Particularly, SH for fruity smells may be a sensitive indicator of FLD.

Biosensors for Parkinson's Disease: Where Are We Now, and Where Do We Need to Go?

Parkinson's Disease is the second most common neurological disease in the United States, yet there is no cure, no pinpointed cause, and no definitive diagnostic procedure. Parkinson's is typically diagnosed when patients present with motor symptoms such as slowness of movement and tremors. However, none of these are specific to Parkinson's, and a confident diagnosis of Parkinson's is typically only achieved when 60-80% of dopaminergic neurons are no longer functioning, at which point much of the damage to the brain is irreversible. This Perspective details ongoing efforts and accomplishments in biosensor research with the goal of overcoming these issues for Parkinson's diagnosis and care, with a focus on the potential impact of early diagnosis and associated opportunities to pinpoint a cause and a cure. We critically analyze the strengths and shortcomings of current technologies and discuss the ideal characteristics of a diagnostic technology toolbox to guide future research decisions in this space. Finally, we assess what role biosensors can play in facilitating precision medicine for Parkinson's patients.

Plasma NfL, GFAP, amyloid, and p-tau species as Prognostic biomarkers in Parkinson's disease

INTRODUCTION

The prognostic role of plasma neurofilament light chain (NfL), phospho-tau, beta-amyloid, and GFAP is still debated in Parkinson's disease (PD).

METHODS

Plasma p-tau181, p-tau231, Aβ1-40, Aβ1-42, GFAP, and NfL were measured by SIMOA in 136 PD with 2.9 + 1.7 years of follow-up and 76 controls. Differences in plasma levels between controls and PD and their correlation with clinical severity and progression rates were evaluated using linear regression analyses.

RESULTS

Patients exhibited similar distribution of plasma biomarkers but higher P-tau181, P-tau231 and lower Aβ1-42 compared with controls. NfL and GFAP correlated with baseline motor and non-motor severity measures. At follow-up, NfL emerged as the best predictor of progression with marginal effect of GFAP and p-tau181 adjusting for age, sex, disease duration, and baseline motor severity.

CONCLUSION

The present findings confirmed plasma NfL as best predictor of progression in PD, with a marginal role of p-tau181 and GFAP.

Spatiotemporal Progression Patterns of Dopamine Availability and Deep Gray Matter Volume in Parkinson Disease-Related Cognitive Impairment

BACKGROUND AND OBJECTIVES

Cognitive impairment is a frequent nonmotor symptom in patients with Parkinson disease (PD), and early cognitive decline is often attributed to dopaminergic system dysfunction. We aimed to explore spatiotemporal progression patterns of striatal dopamine availability and regional brain volume based on cognitive status among patients with PD.

METHODS

This retrospective, cross-sectional study included patients with newly diagnosed PD who were not taking medication for this condition who visited a university-affiliated hospital in Seoul between January 2018 and December 2020. Patients were classified as having normal cognition (PD-NC), mild cognitive impairment (PD-MCI), or PD dementia (PDD) based on Seoul Neuropsychological Screening Battery-II, which includes 31 subsets covering activities of daily living and 5 cognitive domains. They all had brain imaging with MRI and PET with 18F-N-(3-fluoropropyl)-2beta-carbon ethoxy-3beta-(4-iodophenyl) nortropane at baseline. Subsequently, standardized uptake value ratios (SUVRs) for regional dopamine availability and regional gray matter volumes were obtained using automated segmentation. These metrics were compared across cognitive status groups, and spatiotemporal progression patterns were analyzed using the Subtype and Stage Inference machine learning technique.

RESULTS

Among 168 patients (mean age, 73.3 ± 6.1 years; 81 [48.2%] women), 65 had PD-NC, 65 had PD-MCI, and 38 had PDD. Patients with PD-MCI exhibited lower SUVRs (3.61 ± 1.31, p < 0.001) in the caudate than patients with PD-NC (4.43 ± 1.21) but higher SUVRs than patients with PDD (2.39 ± 1.06). Patients with PD-NC had higher thalamic SUVRs (1.55 ± 0.16, p < 0.001) than patients with both PD-MCI (1.45 ± 0.16) and PDD (1.38 ± 0.19). Regional deep gray matter volumes of the caudate (p = 0.015), putamen (p = 0.012), globus pallidus (p < 0.001), thalamus (p < 0.001), hippocampus (p < 0.001), and amygdala (p < 0.001) were more reduced in patients with PD-MCI or PDD than in patients with PD-NC, and the SUVR of the caudate correlated with caudate volume (r = 0.187, p = 0.015). Hippocampal atrophy was the initial change influencing cognitive impairment. The reduced dopamine availability of the thalamus preceded reductions in volume across most deep gray matter regions.

DISCUSSION

Our finding underscores the association between decreased dopamine availability and volume of the caudate and thalamus with cognitive dysfunction in PD. The dopamine availability of the caudate and thalamus was reduced before the volume of the caudate and thalamus was decreased, highlighting the spatiotemporal association between dopaminergic and structural pathology in cognitive impairment in PD.

Machine learning for predicting cognitive decline within five years in Parkinson's disease: Comparing cognitive assessment scales with DAT SPECT and clinical biomarkers

OBJECTIVE

Parkinson's disease (PD) is an age-related neurodegenerative condition characterized mostly by motor symptoms. Although a wide range of non-motor symptoms (NMS) are frequently experienced by PD patients. One of the important and common NMS is cognitive impairment, which is measured using different cognitive scales. Monitoring cognitive impairment and its decline in PD is essential for patient care and management. In this study, our goal is to identify the most effective cognitive scale in predicting cognitive decline over a 5-year timeframe initializing clinical biomarkers and DAT SPECT.

METHODS

Machine Learning has previously shown superior performance in image and clinical data classification and detection. In this study, we propose to use machine learning with different types of data, such as DAT SPECT and clinical biomarkers, to predict PD-CD based on various cognitive scales. We collected 330 DAT SPECT images and their clinical data in baseline, years 2,3,4, and 5 from Parkinson's Progression Markers Initiative (PPMI). We then designed a 3D Autoencoder to extract deep radiomic features (DF) from DAT SPECT images, and we then concatenated it with 17 clinical features (CF) to predict cognitive decline based on Montreal Cognitive Assessment (MoCA) and The Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS-I).

RESULTS

The utilization of MoCA as a cognitive decline scale yielded better performance in various years compared to MDS-UPDRS-I. In year 4, the application of the deep radiomic feature resulted in the highest achievement, with a cross-validation AUC of 89.28, utilizing the gradient boosting classifier. For the MDS-UPDRS-I scale, the highest achievement was obtained by utilizing the deep radiomic feature, resulting in a cross-validation AUC of 81.34 with the random forest classifier.

CONCLUSIONS

The study findings indicate that the MoCA scale may be a more effective predictor of cognitive decline within 5 years compared to MDS-UPDRS-I. Furthermore, deep radiomic features had better performance compared to sole clinical biomarkers or clinical and deep radiomic combined. These results suggest that using the MoCA score and deep radiomic features extracted from DAT SPECT could be a promising approach for identifying individuals at risk for cognitive decline in four years. Future research is needed to validate these findings and explore their utility in clinical practice.

Neuroimaging and fluid biomarkers in Parkinson's disease in an era of targeted interventions

A major challenge in Parkinson's disease is the variability in symptoms and rates of progression, underpinned by heterogeneity of pathological processes. Biomarkers are urgently needed for accurate diagnosis, patient stratification, monitoring disease progression and precise treatment. These were previously lacking, but recently, novel imaging and fluid biomarkers have been developed. Here, we consider new imaging approaches showing sensitivity to brain tissue composition, and examine novel fluid biomarkers showing specificity for pathological processes, including seed amplification assays and extracellular vesicles. We reflect on these biomarkers in the context of new biological staging systems, and on emerging techniques currently in development.

PE/PPE mutations in the transmission of Mycobacterium tuberculosis in China revealed by whole genome sequencing

OBJECTIVE

This study aims to examine the impact of PE/PPE gene mutations on the transmission of Mycobacterium tuberculosis (M. tuberculosis) in China.

METHODS

We collected the whole genome sequencing (WGS) data of 3202 M. tuberculosis isolates in China from 2007 to 2018 and investigated the clustering of strains from different lineages. To evaluate the potential role of PE/PPE gene mutations in the dissemination of the pathogen, we employed homoplastic analysis to detect homoplastic single nucleotide polymorphisms (SNPs) within these gene regions. Subsequently, logistic regression analysis was conducted to analyze the statistical association.

RESULTS

Based on nationwide M. tuberculosis WGS data, it has been observed that the majority of the M. tuberculosis burden in China is caused by lineage 2 strains, followed by lineage 4. Lineage 2 exhibited a higher number of transmission clusters, totaling 446 clusters, of which 77 were cross-regional clusters. Conversely, there were only 52 transmission clusters in lineage 4, of which 9 were cross-regional clusters. In the analysis of lineage 2 isolates, regression results showed that 4 specific gene mutations, PE4 (position 190,394; c.46G > A), PE_PGRS10 (839,194; c.744 A > G), PE16 (1,607,005; c.620T > G) and PE_PGRS44 (2,921,883; c.333 C > A), were significantly associated with the transmission of M. tuberculosis. Mutations of PE_PGRS10 (839,334; c.884 A > G), PE_PGRS11 (847,613; c.1455G > C), PE_PGRS47 (3,054,724; c.811 A > G) and PPE66 (4,189,930; c.303G > C) exhibited significant associations with the cross-regional clusters. A total of 13 mutation positions showed a positive correlation with clustering size, indicating a positive association. For lineage 4 strains, no mutations were found to enhance transmission, but 2 mutation sites were identified as risk factors for cross-regional clusters. These included PE_PGRS4 (338,100; c.974 A > G) and PPE13 (976,897; c.1307 A > C).

CONCLUSION

Our results indicate that some PE/PPE gene mutations can increase the risk of M. tuberculosis transmission, which might provide a basis for controlling the spread of tuberculosis.

Hypoperfusion in Alzheimer's Disease-Prone Regions and Dementia Conversion in Parkinson's Disease

PURPOSE OF THE REPORT

Although early detection of individuals at risk of dementia conversion is important in patients with Parkinson's disease (PD), there is still no consensus on neuroimaging biomarkers for predicting future cognitive decline. We aimed to investigate whether cerebral perfusion patterns on early-phase 18 F-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane ( 18 F-FP-CIT) PET have the potential to serve as a neuroimaging predictor for early dementia conversion in patients with PD.

MATERIALS AND METHODS

In this retrospective analysis, we enrolled 187 patients with newly diagnosed PD who underwent dual-phase 18 F-FP-CIT PET at initial assessment and serial cognitive assessments during the follow-up period (>5 years). Patients with PD were classified into 2 groups: the PD with dementia (PDD)-high-risk (PDD-H; n = 47) and the PDD-low-risk (PDD-L; n = 140) groups according to dementia conversion within 5 years of PD diagnosis. We explored between-group differences in the regional uptake in the early-phase 18 F-FP-CIT PET images. We additionally performed a linear discriminant analysis to develop a prediction model for early PDD conversion.

RESULTS

The PDD-H group exhibited hypoperfusion in Alzheimer's disease (AD)-prone regions (inferomedial temporal and posterior cingulate cortices, and insula) compared with the PDD-L group. A prediction model using regional uptake in the right entorhinal cortex, left amygdala, and left isthmus cingulate cortex could optimally distinguish the PDD-H group from the PDD-L group.

CONCLUSIONS

Regional hypoperfusion in the AD-prone regions on early-phase 18 F-FP-CIT PET can be a useful biomarker for predicting early dementia conversion in patients with PD.

Radionuclide Imaging of the Neuroanatomical and Neurochemical Substrate of Cognitive Decline in Parkinson's Disease

Cognitive impairment is a frequent manifestation of Parkinson's disease (PD), resulting in decrease in patients' quality of life and increased societal and economic burden. However, cognitive decline in PD is highly heterogenous and the mechanisms are poorly understood. Radionuclide imaging techniques like positron emission tomography (PET) and single photon emission computed tomography (SPECT) have been used to investigate the neurochemical and neuroanatomical substrate of cognitive decline in PD. These techniques allow the assessment of different neurotransmitter systems, changes in brain glucose metabolism, proteinopathy, and neuroinflammation in vivo in PD patients. Here, we review current radionuclide imaging research on cognitive deficit in PD with a focus on predicting accelerating cognitive decline. This research could assist in the development of prognostic biomarkers for patient stratification and have utility in the development of ameliorative or disease-modifying therapies targeting cognitive deficit in PD.

Mild cognitive impairment among LRRK2 and GBA1 patients with Parkinson's disease

BACKGROUND

Mild cognitive impairment (MCI) is common in Parkinson's disease (PD). We aimed to assess the incidence of MCI among patients with PD, carriers of mutations in LRRK2 and GBA1 genes, based on the movement disorder society (MDS) criteria for the diagnosis of MCI in early-stage PD.

METHODS

Patients with PD were included if they scored ≤2 on the Hoehn and Yahr and ≤6 years since motor symptom onset. A group of age and gender matched healthy adults served as controls. A neuropsychological cognitive battery was used covering five cognitive domains (executive functions, working memory, memory, visuospatial and language). MCI was explored while applying two methods (level I and II). Frequency of MCI was assessed in comparison between groups.

RESULTS

70 patients with idiopathic PD (iPD) (68 % males), 42 patients with LRRK2-PD (61 % males), 83 patients with GBA1-PD (63 % males) and 132 age and gender matched controls (61 % males), participated in this study. PD groups were similar in clinical characteristics. Level I criteria were positive in 57.5 % of iPD, 43 % of LRRK2-PD and 63.4 % of the GBA1-PD (p = 0.071). Level II criteria was met by 39 % of iPD, 14 % LRRK2-PD and 41 % of GBA1-PD (p < 0.001), when using a 2 standard-deviation (SD) threshold. GBA1-PD and iPD showed impairments on multiple domains even in the more conservative 2 SD, reflecting MCI.

CONCLUSIONS

The majority of our PD cohort was classified as MCI when assessed with strict criteria. GBA1-PD and iPD showed a more widespread pattern of MCI compared with LRRK2-PD.

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review

BACKGROUND

Parkinson's Disease is the second most common neurological disease in over 60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to 20 years post-diagnosis. Processes for detection and diagnosis of cognitive impairments are not sufficient to predict decline at an early stage for significant impact. Ageing populations, neurologist shortages and subjective interpretations reduce the effectiveness of decisions and diagnoses. Researchers are now utilising machine learning for detection and diagnosis of cognitive impairment based on symptom presentation and clinical investigation. This work aims to provide an overview of published studies applying machine learning to detecting and diagnosing cognitive impairment, evaluate the feasibility of implemented methods, their impacts, and provide suitable recommendations for methods, modalities and outcomes.

METHODS

To provide an overview of the machine learning techniques, data sources and modalities used for detection and diagnosis of cognitive impairment in Parkinson's Disease, we conducted a review of studies published on the PubMed, IEEE Xplore, Scopus and ScienceDirect databases. 70 studies were included in this review, with the most relevant information extracted from each. From each study, strategy, modalities, sources, methods and outcomes were extracted.

RESULTS

Literatures demonstrate that machine learning techniques have potential to provide considerable insight into investigation of cognitive impairment in Parkinson's Disease. Our review demonstrates the versatility of machine learning in analysing a wide range of different modalities for the detection and diagnosis of cognitive impairment in Parkinson's Disease, including imaging, EEG, speech and more, yielding notable diagnostic accuracy.

CONCLUSIONS

Machine learning based interventions have the potential to glean meaningful insight from data, and may offer non-invasive means of enhancing cognitive impairment assessment, providing clear and formidable potential for implementation of machine learning into clinical practice.

Parkinson's disease with anxiety: clinical characteristics and their correlation with oxidative stress, inflammation, and pathological proteins

OBJECTIVE

This study was performed to explore the differences in the clinical characteristics and oxidative stress indicators, inflammatory factors, and pathological proteins in serum between Parkinson's disease (PD) with anxiety (PD-A) and with no anxiety (PD-NA) patients, and further correlations among clinical characteristics and above variables were analyzed in PD-A and PD-NA groups.

METHODS

A total of 121 patients with PD were enrolled in this study and assessed by the Hamilton Anxiety Scale (14 items) (HAMA-14). These patients were divided into PD-A and PD-NA groups according to a cut-off point of 7 of HAMA-14. Demographic variables were collected, and clinical symptoms were assessed by multiple rating scales. The levels of free radicals, inflammatory factors, and pathological proteins in serum were measured by chemical colorimetric method and enzyme-linked immunosorbent assay (ELISA). The differences of above variables were compared between PD-A and PD-NA groups, and the correlations of clinical symptoms with the abovevariables were analyzed in PD-A and PD-NA groups.

RESULTS

The frequency of PD-A was 62.81%. PD-A group exhibited significantly impaired motor dysfunction and multiple non-motor symptoms, including fatigue, sleep behavior disorder, restless leg syndrome and autonomic dysfunction, and dramatically compromised activities of daily living compard with PD-NA group. PD-A group displayed prominently increasedlevels of hydroxyl radical (·OH) and tumor necrosis factor (TNF)-α, and a decreased nitric oxide (NO) level in serum compared with PD-NA group (P<0.001, P = 0.001, P= 0.027, respectively). ·OH, NO, and TNF-α were identified as the risk factors of PD-A (OR = 1.005, P = 0.036; OR = 0.956, P = 0.017; OR = 1.039, P = 0.033, respectively). In PD patients, HAMA-14 score was significantly and positively correlated with the levels of ·OH and TNF-α in serum (P<0.001, P = 0.002, respectively). In PD-A group, ·OH level was significantly and negatively correlated with Aβ1-42 level, while TNF-α level was significantly and positively correlated with P-tau (S396) level in serum.

CONCLUSIONS

The frequency of PD-A is high. PD-A patients present more severe motor dysfunction and multiple non-motor symptoms, and poorer activities of daily living. The increased levels of ·OH and TNF-α levels and the decreased NO level in serum are all associated with more severe anxiety in PD patients.Findings from this study may provide in-depth insights into the clinical characteristics, underlying mechanisms of PD-A, and potential correlations among anxiety, oxidative stress, inflammation, and cognitive decline in PD patients.

Prediction Model for Cognitive Impairment among Disabled Older Adults: A Development and Validation Study

Disabled older adults exhibited a higher risk for cognitive impairment. Early identification is crucial in alleviating the disease burden. This study aims to develop and validate a prediction model for identifying cognitive impairment among disabled older adults. A total of 2138, 501, and 746 participants were included in the development set and two external validation sets. Logistic regression, support vector machine, random forest, and XGBoost were introduced to develop the prediction model. A nomogram was further established to demonstrate the prediction model directly and vividly. Logistic regression exhibited better predictive performance on the test set with an area under the curve of 0.875. It maintained a high level of precision (0.808), specification (0.788), sensitivity (0.770), and F1-score (0.788) compared with the machine learning models. We further simplified and established a nomogram based on the logistic regression, comprising five variables: age, daily living activities, instrumental activity of daily living, hearing impairment, and visual impairment. The areas under the curve of the nomogram were 0.871, 0.825, and 0.863 in the internal and two external validation sets, respectively. This nomogram effectively identifies the risk of cognitive impairment in disabled older adults.

Muscle density, but not size, is independently associated with cognitive health in older adults with hip fractures

Emerging evidence indicates a complex interplay between skeletal muscle and cognitive function. Despite the known differences between muscle quantity and quality, which can be measured via computed tomography (CT), the precise nature of their associations with cognitive performance remain underexplored. To investigate the links between muscle size and density and cognitive impairment (CI) in the older adults with hip fractures, we conducted a post hoc, cross-sectional analysis within a prospective cohort study on 679 patients with hip fractures over 65. Mini-Mental State Examination (MMSE) and routine hip CT imaging were utilized to assess cognition function and muscle characteristics in older adults with hip fractures. The CT scans provided data on cross-sectional area and attenuation for the gluteus maximus (G.MaxM) and the combined gluteus medius and minimus (G.Med/MinM). Participants were categorized into CI and non-CI groups based on education levels and MMSE scores. Multivariate logistic regressions, propensity score (PS) methods, and subgroup analysis were employed to analyze associations and validate findings. This study included 123 participants (81.6 ± 6.8 years, 74% female) with CI and 556 participants (78.5 ± 7.7 years, 72% female) without. Compared to the non-CI group, muscle parameters, especially density, were significantly lower in the CI group. Specifically, G.Med/Min muscle density, but not size was robustly associated with CI (odds ratio (OR) = 0.77, 95% confidence interval = 0.62-0.96, P = 0.02), independent of other medical situations. Sensitivity analysis corroborated that G.Med/Min muscle density was consistently lower in the CI group than the non-CI group, as evidenced in the PS matched (P = 0.024) and weighted cohort (P = 0.033). Enhanced muscle parameters, particularly muscle density in the G.Med/MinM muscle, correlate with a lower risk of CI. Muscle density demonstrates a stronger association with cognitive performance than muscle size, highlighting its potential as a key focus in future cognitive health research.

Neuroimaging and plasma evidence of early white matter loss in Parkinson's disease with poor outcomes

Parkinson's disease is a common and debilitating neurodegenerative disorder, with over half of patients progressing to postural instability, dementia or death within 10 years of diagnosis. However, the onset and rate of progression to poor outcomes is highly variable, underpinned by heterogeneity in underlying pathological processes. Quantitative and sensitive measures predicting poor outcomes will be critical for targeted treatment, but most studies to date have been limited to a single modality or assessed patients with established cognitive impairment. Here, we used multimodal neuroimaging and plasma measures in 98 patients with Parkinson's disease and 28 age-matched controls followed up over 3 years. We examined: grey matter (cortical thickness and subcortical volume), white matter (fibre cross-section, a measure of macrostructure; and fibre density, a measure of microstructure) at whole-brain and tract level; structural and functional connectivity; and plasma levels of neurofilament light chain and phosphorylated tau 181. We evaluated relationships with subsequent poor outcomes, defined as development of mild cognitive impairment, dementia, frailty or death at any time during follow-up, in people with Parkinson's disease. We show that extensive white matter macrostructural changes are already evident at baseline assessment in people with Parkinson's disease who progress to poor outcomes (n = 31): with up to 19% reduction in fibre cross-section in multiple tracts, and a subnetwork of reduced structural connectivity strength, particularly involving connections between right frontoparietal and left frontal, right frontoparietal and left parietal and right temporo-occipital and left parietal modules. In contrast, grey matter volumes and functional connectivity were preserved in people with Parkinson's disease with poor outcomes. Neurofilament light chain, but not phosphorylated tau 181 levels were increased in people with Parkinson's disease with poor outcomes, and correlated with white matter loss. These findings suggest that imaging sensitive to white matter macrostructure and plasma neurofilament light chain may be useful early markers of poor outcomes in Parkinson's disease. As new targeted treatments for neurodegenerative disease are emerging, these measures show important potential to aid patient selection for treatment and improve stratification for clinical trials.

Neurocognitive Impairment and Social Cognition in Parkinson's Disease Patients

In addition to motor symptoms, neurocognitive impairment (NCI) affects patients with prodromal Parkinson's disease (PD). NCI in PD ranges from subjective cognitive complaints to dementia. The purpose of this review is to present the available evidence of NCI in PD and highlight the heterogeneity of NCI phenotypes as well as the range of factors that contribute to NCI onset and progression. A review of publications related to NCI in PD up to March 2023 was performed using PubMed/Medline. There is an interconnection between the neurocognitive and motor symptoms of the disease, suggesting a common underlying pathophysiology as well as an interconnection between NCI and non-motor symptoms, such as mood disorders, which may contribute to confounding NCI. Motor and non-motor symptom evaluation could be used prognostically for NCI onset and progression in combination with imaging, laboratory, and genetic data. Additionally, the implications of NCI on the social cognition of afflicted patients warrant its prompt management. The etiology of NCI onset and its progression in PD is multifactorial and its effects are equally grave as the motor effects. This review highlights the importance of the prompt identification of subjective cognitive complaints in PD patients and NCI management.

Long-term exposure to fine particulate matter constituents and cognitive impairment among older adults: An 18-year Chinese nationwide cohort study

BACKGROUND

Although growing evidence has shown independent links of long-term exposure to fine particulate matter (PM2.5) with cognitive impairment, the effects of its constituents remain unclear. This study aims to explore the associations of long-term exposure to ambient PM2.5 constituents' mixture with cognitive impairment in Chinese older adults, and to further identify the main contributor.

METHODS

15,274 adults ≥ 65 years old were recruited by the Chinese Longitudinal Healthy Longevity Study (CLHLS) and followed up through 7 waves during 2000-2018. Concentrations of ambient PM2.5 and its constituents (i.e., black carbon [BC], organic matter [OM], ammonium [NH4+], sulfate [SO42-], and nitrate [NO3-]) were estimated by satellite retrievals and machine learning models. Quantile-based g-computation model was employed to assess the joint effects of a mixture of 5 PM2.5 constituents and their relative contributions to cognitive impairment. Analyses stratified by age group, sex, residence (urban vs. rural), and region (north vs. south) were performed to identify vulnerable populations.

RESULTS

During the average 3.03 follow-up visits (89,296.9 person-years), 4294 (28.1%) participants had developed cognitive impairment. The adjusted hazard ratio [HR] (95% confidence interval [CI]) for cognitive impairment for every quartile increase in mixture exposure to 5 PM2.5 constituents was 1.08 (1.05-1.11). BC held the largest index weight (0.69) in the positive direction in the qg-computation model, followed by OM (0.31). Subgroup analyses suggested stronger associations in younger old adults and rural residents.

CONCLUSION

Long-term exposure to ambient PM2.5, particularly its constituents BC and OM, is associated with an elevated risk of cognitive impairment onset among Chinese older adults.

Cognitive dysfunction in de novo Parkinson disease: Remitting vs. progressive cognitive impairment

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.

Protein-protein interactions regulating α-synuclein pathology

Parkinson's disease (PD) is a neurodegenerative disease characterized by the degeneration of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the formation of Lewy bodies (LBs). The main proteinaceous component of LBs is aggregated α-synuclein (α-syn). However, the mechanisms underlying α-syn aggregation are not yet fully understood. Converging lines of evidence indicate that, under certain pathological conditions, various proteins can interact with α-syn and regulate its aggregation. Understanding these protein-protein interactions is crucial for unraveling the molecular mechanisms contributing to PD pathogenesis. In this review we provide an overview of the current knowledge on protein-protein interactions that regulate α-syn aggregation. Additionally, we briefly summarize the methods used to investigate the influence of protein-protein interactions on α-syn aggregation and propagation.

Identification of Parkinson's disease subtypes with distinct brain atrophy progression and its association with clinical progression

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.

Mild Cognitive Impairment Progression and Alzheimer's Disease Risk: A Comprehensive Analysis of 3553 Cases over 203 Months

This study aimed to elucidate the long-term progression of mild cognitive impairment (MCI) within a comprehensive longitudinal dataset, distinguish it from healthy aging, explore the influence of a dementia subtype on this progression, and identify potential contributing factors. Patients with prodromal and preclinical cases underwent regular neuropsychological assessments utilizing various tools. The study included a total of 140 participants with MCI, categorized into Alzheimer's disease (AD) and non-AD subtypes. Our dataset revealed an overall progression rate of 92.8% from MCI to the clinical stage of dementia during the follow-up period, with an annual rate of 15.7%. Notably, all prodromal cases of Lewy body dementia/Parkinson's disease (LBD/PDD) and frontotemporal dementia (FTD) advanced to clinical stages, whereas 7% of vascular dementia (VaD) cases and 8.4% of AD cases remained in the prodromal stage throughout follow-up. Furthermore, we observed a faster progression rate in MCI-AD cases compared to non-AD sufferers (53.9% vs. 35.5%, Entropy: 0.850). This study revealed significant cognitive changes in individuals with MCI over time. The mini-mental state examination (MMSE), global deterioration scale (GDS), and calculation tests were the most effective tests for evaluation of MCI. These findings may offer valuable insights for the development of personalized interventions and management strategies for individuals with MCI.

Mild Behavioral Impairment in Parkinson's Disease: An Updated Review on the Clinical, Genetic, Neuroanatomical, and Pathophysiological Aspects

Neuropsychiatric symptoms (NPS), including depression, anxiety, apathy, visual hallucinations, and impulse control disorders, are very common during the course of Parkinson's disease (PD), occurring even at the prodromal and premotor stages. Mild behavioral impairment (MBI) represents a recently described neurobehavioral syndrome, characterized by the emergence of persistent and impactful NPS in later life, reflecting arisk of dementia. Accumulating evidence suggests that MBI is highly prevalent in non-demented patients with PD, also being associated with an advanced disease stage, more severe motor deficits, as well as global and multiple-domain cognitive impairment. Neuroimaging studies have revealed that MBI in patients with PD may be related todistinct patterns of brain atrophy, altered neuronal connectivity, and distribution of dopamine transporter (DAT) depletion, shedding more light on its pathophysiological background. Genetic studies in PD patients have also shown that specific single-nucleotide polymorphisms (SNPs) may be associated with MBI, paving the way for future research in this field. In this review, we summarize and critically discuss the emerging evidence on the frequency, associated clinical and genetic factors, as well as neuroanatomical and neurophysiological correlates of MBI in PD, aiming to elucidate the underlying pathophysiology and its potential role as an early "marker" of cognitive decline, particularly in this population. In addition, we aim to identify research gaps, and propose novel relative areas of interest that could aid in our better understanding of the relationship of this newly defined diagnostic entity with PD.

Risk Factors for Postoperative Delirium Severity After Deep Brain Stimulation Surgery in Parkinson's Disease

Background

Postoperative delirium (POD) is a serious complication following deep brain stimulation (DBS) but only received little attention. Its main risk factors are higher age and preoperative cognitive deficits. These are also main risk factors for long-term cognitive decline after DBS in Parkinson's disease (PD).

Objective

To identify risk factors for POD severity after DBS surgery in PD.

Methods

57 patients underwent DBS (21 female; age 60.2±8.2; disease duration 10.5±5.9 years). Preoperatively, general, PD- and surgery-specific predictors were recorded. Montreal Cognitive Assessment and the neuropsychological test battery CANTAB ConnectTM were used to test domain-specific cognition. Volumes of the cholinergic basal forebrain were calculated with voxel-based morphometry. POD severity was recorded with the delirium scales Confusion Assessment Method for Intensive Care Unit (CAM-ICU) and Nursing Delirium Scale (NU-DESC). Spearman correlations were calculated for univariate analysis of predictors and POD severity and linear regression with elastic net regularization and leave-one-out cross-validation was performed to fit a multivariable model.

Results

21 patients (36.8%) showed mainly mild courses of POD following DBS. Correlation between predicted and true POD severity was significant (spearman rho = 0.365, p = 0.001). Influential predictors were age (p < 0.001), deficits in attention and motor speed (p = 0.002), visual learning (p = 0.036) as well as working memory (p < 0.001), Nucleus basalis of Meynert volumes (p = 0.003) and burst suppression (p = 0.005).

Conclusions

General but also PD- and surgery-specific factors were predictive of POD severity. These findings underline the multifaceted etiology of POD after DBS in PD. Valid predictive models must therefore consider general, PD- and surgery-specific factors.

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.

Relationship of irisin with disease severity and dopamine uptake in Parkinson's disease patients

BACKGROUND

This study was designed to investigate the relationship of irisin with the severity of Parkinson's disease (PD) and dopamine (DOPA) uptake in patients with PD and to understand the role of irisin in PD.

METHODS

The plasma levels of irisin and α-syn were measured by enzyme-linked immunosorbent assay (ELISA). Motor and nonmotor symptoms were assessed with the relevant scales. DOPA uptake was measured with DOPA positron emission tomography (PET)/magnetic resonance imaging (MRI).

RESULTS

The plasma levels of α-syn and irisin in patients with PD gradually increased and decreased, respectively, with the progression of the disease. There was a negative correlation between plasma α-syn and irisin levels in patients with PD. The level of irisin in plasma was negatively correlated with Unified Parkinson's Disease Rating Scale (UPDRS)-III scores and positively correlated with Montreal Cognitive Assessment (MoCA) scores. The striatal/occipital lobe uptake ratios (SORs) of the ipsilateral and contralateral caudate nucleus and anterior and posterior putamen in the high-irisin group were significantly higher than those in the low-irisin group, and irisin levels in the caudate nucleus and anterior and posterior putamen contralateral to the affected limb were lower than those on the ipsilateral side. The level of irisin was positively correlated with the SORs of the ipsilateral and contralateral caudate nucleus and putamen in PD patients.

CONCLUSIONS

Irisin plays a neuroprotective role by decreasing the level of α-syn. Irisin is negatively correlated with the severity of motor symptoms and cognitive impairment. More importantly, irisin can improve DOPA uptake in the striatum of patients with PD, especially on the side contralateral to the affected limb.

Prediction of Parkinson's Disease Using Machine Learning Methods

The detection of Parkinson's disease (PD) in its early stages is of great importance for its treatment and management, but consensus is lacking on what information is necessary and what models should be used to best predict PD risk. In our study, we first grouped PD-associated factors based on their cost and accessibility, and then gradually incorporated them into risk predictions, which were built using eight commonly used machine learning models to allow for comprehensive assessment. Finally, the Shapley Additive Explanations (SHAP) method was used to investigate the contributions of each factor. We found that models built with demographic variables, hospital admission examinations, clinical assessment, and polygenic risk score achieved the best prediction performance, and the inclusion of invasive biomarkers could not further enhance its accuracy. Among the eight machine learning models considered, penalized logistic regression and XGBoost were the most accurate algorithms for assessing PD risk, with penalized logistic regression achieving an area under the curve of 0.94 and a Brier score of 0.08. Olfactory function and polygenic risk scores were the most important predictors for PD risk. Our research has offered a practical framework for PD risk assessment, where necessary information and efficient machine learning tools were highlighted.

Structural connectivity from DTI to predict mild cognitive impairment in de novo Parkinson's disease

BACKGROUND

Early detection of Parkinson's disease (PD) patients at high risk for mild cognitive impairment (MCI) can help with timely intervention. White matter structural connectivity is considered an early and sensitive indicator of neurodegenerative disease.

OBJECTIVES

To investigate whether baseline white matter structural connectivity features from diffusion tensor imaging (DTI) of de novo PD patients can help predict PD-MCI conversion at an individual level using machine learning methods.

METHODS

We included 90 de novo PD patients who underwent DTI and 3D T1-weighted imaging. Elastic net-based feature consensus ranking (ENFCR) was used with 1000 random training sets to select clinical and structural connectivity features. Linear discrimination analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN) and naïve Bayes (NB) classifiers were trained based on features selected more than 500 times. The area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN) and specificity (SPE) were used to evaluate model performance.

RESULTS

A total of 57 PD patients were classified as PD-MCI nonconverters, and 33 PD patients were classified as PD-MCI converters. The models trained with clinical data showed moderate performance (AUC range: 0.62-0.68; ACC range: 0.63-0.77; SEN range: 0.45-0.66; SPE range: 0.64-0.84). Models trained with structural connectivity (AUC range, 0.81-0.84; ACC range, 0.75-0.86; SEN range, 0.77-0.91; SPE range, 0.71-0.88) performed similar to models that were trained with both clinical and structural connectivity data (AUC range, 0.81-0.85; ACC range, 0.74-0.85; SEN range, 0.79-0.91; SPE range, 0.70-0.89).

CONCLUSIONS

Baseline white matter structural connectivity from DTI is helpful in predicting future MCI conversion in de novo PD patients.

Mild cognitive impairment in patients with Parkinson´s disease and the analysis of associated factors

OBJECTIVES

This research targeted to understand the impact of clinical findings, non-motor symptoms, white matter hyperintensities (WMHs), and metabolic features on cognition in Parkinson's disease patients with mild cognitive impairment (PD-MCI).

METHODS

Sixty-one PD patients sundered into two groups: PD-MCI and normal cognition (PD-NC). We assessed cognition using Montreal Cognitive Assessment-TR (MoCA-TR) and Frontal Assessment Battery (FAB). We used the modified Hoehn&Yahr staging scale (mH&Y), Unified Parkinson's Disease Rating Scale (UPDRS), Freezing of Gait questionnaire, Beck Depression Inventory, Parkinson's disease sleep scale-2, Pittsburgh sleep quality index, Epworth sleepiness scale, and Non-motor symptoms questionnaire to evaluate all patients. We used the Fazekas scale to evaluate the WMHs and also investigated all laboratory parameters affecting cognitive functions.

RESULTS

Duration of disease, UPDRS-Motor part, age, disease stage, and daytime sleepiness were dramatically higher in the PD-MCI group than in PD-NC (p < 0.05). WMHs and homocysteine were higher in the PD-MCI group than in the controls (p = 0.016 and p < 0.001, respectively). There was a negative correlation between cognition and duration of disease, age, disease stage, UPDRS-Motor scale, daytime drowsiness, WMHs and homocysteine levels. Homocysteine was negatively related to visuospatial/executive functions (r=-0.303, p = 0.021). WMHs were correlated with global cognition (p =.000 r = .-542), language (p = .001, r = -.434), and delayed recall (p = .011, r = -.332).

DISCUSSION

Mild cognitive impairment is a widespread clinical situation of PD patients and often presents before the motor symptoms. Revealing curable causes that affect cognition before the development of PD-related dementia is crucial in controlling motor findings and reducing the burden of the caretakers.

Predictors of dopamine dysregulation syndrome in patients with early Parkinson's disease

BACKGROUND

Dopamine dysregulation syndrome (DDS) is a complication of Parkinson's disease (PD) that seriously affects the quality of life of PD patients. Currently, the risk factors for DDS are poorly known, and it is critical to identify them in the early stages of PD.

OBJECTIVE

To explore the incidence of and risk factors for DDS in patients with early PD.

METHODS

A retrospective cohort study was conducted on the general data, clinical features, and imaging data of patients with early PD in the PPMI database. Multivariate Cox regression analysis was performed to analyze the risk factors for the development of DDS in patients with early PD, and Kaplan‒Meier curves examined the frequency and predictors of incident DDS symptoms.

RESULTS

At baseline, 2.2% (n = 6) of patients with early PD developed DDS, and the cumulative incidence rates of DDS during the 5-year follow-up period were 2.8%, 6.4%, 10.8%, 15.5%, and 18.7%, respectively. In the multivariate Cox regression model controlling for age, sex, and drug use, hypersexuality (HR = 3.088; 95% CI: 1.416~6.732; P = 0.005), compulsive eating (HR = 3.299; 95% CI: 1.665~6.534; P = 0.001), compulsive shopping (HR = 3.899; 95% CI: 1.769~8.593; P = 0.001), anxiety (HR = 4.018; 95% CI: 2.136~7.599; P < 0.01), and lower Hoehn-Yahr (H-Y) stage (HR = 0.278; 95% CI: 0.152~0.509; P < 0.01) were independent risk factors for DDS in patients with early PD. PD patients with DDS had lower DAT uptake values than those patients without DDS.

CONCLUSION

Early PD patients with hypersexuality, compulsive eating, compulsive shopping, anxiety, and lower H-Y stage were at increased risk for DDS. The occurrence of DDS may be related to the decrease in the average DAT uptake of the caudate and putamen.

Evolution and Predictive Role of Plasma Alzheimer's Disease-related Pathological Biomarkers in Parkinson's Disease

Plasma Alzheimer's disease-related pathological biomarkers' role in Parkinson's disease (PD) remains unknown. We aimed to determine whether plasma Alzheimer's disease-related biomarkers can predict PD progression. A total of 184 PD patients and 86 healthy controls were included and followed up for 5 years. Plasma phosphorylated tau181 (p-tau181), Aβ40, and Aβ42 were measured at baseline and the 1- and 2-year follow-ups using the Quanterix-single-molecule array. Global cognitive function and motor symptoms were assessed using the Montreal Cognitive Assessment and Unified Parkinson's Disease Rating Scale part III. Genetic analyses were conducted to identify APOE and MAPT genotypes. Plasma p-tau181 levels were higher in PD than healthy controls. APOE-ε4 carriers had lower plasma Aβ42 levels and Aβ42/Aβ40 ratio. The linear mixed-effects models showed that Montreal Cognitive Assessment scores were associated with plasma p-tau181/Aβ42 ratio (β -1.719 [-3.398 to -0.040], p = .045). Higher baseline plasma p-tau181 correlated with faster cognitive decline and motor symptoms deterioration in total patients (β -0.170 [-0.322 to -0.018], p = .029; β 0.329 [0.032 to 0.626], p = .030) and APOE-ε4 carriers (β -0.318 [-0.602 to -0.034], p = .030; β 0.632 [0.017 to 1.246], p = .046), but not in the noncarriers. Higher baseline plasma Aβ40 correlated with faster cognitive decline in total patients (β -0.007 [-0.015 to -0.0001], p = .047) and faster motor symptoms deterioration in total patients (β 0.026 [0.010 to 0.041], p = .001) and APOE-ε4 carriers (β 0.044 [-0.026 to 0.049], p = .020), but not in the noncarriers. The plasma p-tau181/Aβ2 ratio monitors the cognitive status of PD. Higher baseline plasma p-tau181 and Aβ40 predict faster cognitive decline and motor symptoms deterioration in PD, especially in APOE-ε4 carriers.

Clinical biomarkers for Lewy body diseases

Synucleinopathies are a group of neurodegenerative disorders characterized by pathologic aggregates of neural and glial α-synuclein (α-syn) in the form of Lewy bodies (LBs), Lewy neurites, and cytoplasmic inclusions in both neurons and glia. Two major classes of synucleinopathies are LB disease and multiple system atrophy. LB diseases include Parkinson's disease (PD), PD with dementia, and dementia with LBs. All are increasing in prevalence. Effective diagnostics, disease-modifying therapies, and therapeutic monitoring are urgently needed. Diagnostics capable of differentiating LB diseases are based on signs and symptoms which might overlap. To date, no specific diagnostic test exists despite disease-specific pathologies. Diagnostics are aided by brain imaging and cerebrospinal fluid evaluations, but more accessible biomarkers remain in need. Mechanisms of α-syn evolution to pathologic oligomers and insoluble fibrils can provide one of a spectrum of biomarkers to link complex neural pathways to effective therapies. With these in mind, we review promising biomarkers linked to effective disease-modifying interventions.

Aerobic Training Increases Hippocampal Volume and Protects Cognitive Function for Type 2 Diabetes Patients with Normal Cognition

AIM

To evaluate the effects of aerobic training on hippocampal volume and cognitive function in patients with type 2 diabetes mellitus (T2DM) with normal cognition.

MATERIALS AND METHODS

One hundred patients with T2DM aged 60-75 years who met inclusion criteria were randomized into the aerobic training group (n=50) and control group (n=50). The aerobic training group received 1 year of aerobic training, while the control group maintained their lifestyle without additional exercise intervention. The primary outcomes were hippocampal volume measured by MRI and Mini-mental State Examination (MMSE) score or Montreal Cognitive Assessment scale (MoCA) scores.

RESULTS

Eighty-two participants completed the study (aerobic training group, n=40; control group, n=42). There was no significant difference between the two groups at baseline (P>0.05). After one year of moderate aerobic training, increase in total and right hippocampal volume in the aerobic training group were significantly higher than in the control group (P=0.027, P=0.043, respectively). In the aerobic group, total hippocampal volume significantly increased after the intervention compared with baseline (P=0.034). The between-group difference in the change of MMSE and MoCA scores was statistically significant (P=0.015, P=0.027, respectively). Logistic regression showed strong correlations between aerobic training and increase in total hippocampal volume (OR:1.091, [95%CI 0.969, 1.228], P=0.002), improvement of MMSE scores (OR:1.127, [95%CI 1.005, 1.263], P=0.041) or MoCA scores (OR:2.564, [95%CI 2.098.2.973], P=0.045).

CONCLUSIONS

One-year moderate aerobic training increased total and right hippocampal volume and protected cognitive function for T2DM patients with normal cognition. Early intervention focusing on cognition protection should be considered for T2DM patients in clinical settings.

Original research: initial development of a pragmatic tool to estimate cognitive decline risk focusing on potentially modifiable factors in Parkinson's disease

Introduction

Cognitive decline is common in Parkinson's disease (PD). Calculating personalized risk of cognitive decline in PD would allow for appropriate counseling, early intervention with available treatments, and inclusion in disease-modifying trials.

Methods

Data were from the Parkinson's Progression Markers Initiative de novo cohort. Baseline scores were calculated for Lifestyle for Brain Health (LIBRA) and the Montreal Parkinson Risk of Dementia Scale (MoPaRDS) per prior literature and preliminary Parkinson's disease Risk Estimator for Decline In Cognition Tool (pPREDICT) by attributing a point for fourteen posited risk factors. Baseline and 5-year follow-up composite cognitive scores (CCSs) were calculated from a neuropsychological battery and used to define cognitive decliners (PD-decline) versus maintainers (PD-maintain).

Results

The PD-decline group (n = 44) had higher LIBRA (6.76 ± 0.57, p < 0.05), MoPaRDS (2.45 ± 1.41, p < 0.05) and pPREDICT (4.52 ± 1.66, p < 0.05) scores compared to the PD-maintain group (n = 263; LIBRA 4.98 ± 0.20, MoPaRDS 1.68 ± 1.16, pPREDICT 3.38 ± 1.69). Area-under-the-curve (AUC) for LIBRA was 0.64 (95% confidence interval [CI], 0.55-0.73), MoPaRDS was 0.66 (95% CI, 0.58-0.75) and for pPREDICT was 0.68 (95% CI, 0.61-0.76). In linear regression analyses, LIBRA (p < 0.05), MoPaRDS (p < 0.05) and pPREDICT (p < 0.05) predicted change in CCS. Only age stratified by sex (p < 0.05) contributed significantly to the model for LIBRA. Age and presence of hallucinations (p < 0.05) contributed significantly to the model for MoPaRDS. Male sex, older age, excessive daytime sleepiness, and moderate-severe motor symptoms (all p < 0.05) contributed significantly to the model for pPREDICT.

Conclusion

Although MoPaRDS is a PD-specific tool for predicting cognitive decline relying on only clinical features, it does not focus on potentially modifiable risk factors. LIBRA does focus on potentially modifiable risk factors and is associated with prediction of all-cause dementia in some populations, but pPREDICT potentially demonstrates improved performance in cognitive decline risk calculation in individuals with PD and may identify actionable risk factors. As pPREDICT incorporates multiple potentially modifiable risk factors that can be obtained easily in the clinical setting, it is a first step in developing an easily assessable tool for a personalized approach to reduce dementia risk in people with PD.

Remimazolam tosylate's long-term sedative properties in ICU patients on mechanical ventilation: effectiveness and safety

OBJECTIVE

This study compared remimazolam tosylate with propofol or midazolam to assess its safety and effectiveness for long-term sedation of intensive care unit (ICU) patients requiring mechanical ventilation.

METHODS

Adult patients in the ICU receiving sedation and mechanical ventilation for longer than 24 h were included in this single-center, prospective, observational study. Depending on the sedatives they were given, they were split into two groups (midazolam or propofol group; remimazolam group). ICU mortality was the main result. Laboratory tests, adverse events, and the length of ICU stay were considered secondary outcomes.

RESULTS

A total of 106 patients were involved (46 received propofol or midazolam versus 60 received remimazolam). Age (P = 0.182), gender (P = 0.325), and the amount of time between being admitted to the ICU and receiving medication infusion (P = 0.770) did not substantially differ between the two groups. Multivariate analysis revealed no statistically significant difference in ICU mortality between the two groups. The remimazolam group showed less variability in heart rate (P = 0.0021), pH (P = 0.048), bicarbonate (P = 0.0133), lactate (P = 0.0002), arterial blood gas analyses, liver, and kidney function. The Richmond Agitation and Sedation Scale scores, length of ICU stay, and occurrence of adverse events did not exhibit significant differences between the two groups.

CONCLUSION

Remimazolam tosylate did not increase the total inpatient cost, the incidence of adverse events, and ICU mortality in patients with mechanical ventilation. These findings suggest that remimazolam may represent a promising alternative for sedation in the ICU setting.

Atypical brain FDG-PET patterns increase the risk of long-term cognitive and motor progression in Parkinson's disease

INTRODUCTION

Brain hypometabolism patterns have been previously associated with cognitive decline in Parkinson's disease (PD). Our aim is to evaluate the impact of single-subject fluorodeoxyglucose (FDG)-PET brain hypometabolism on long-term cognitive and motor outcomes in PD.

METHODS

Forty-nine non-demented PD patients with baseline brain FDG-PET data underwent an extensive clinical follow-up for 8 years. The ability of FDG-PET to predict long-term cognitive and motor progression was evaluated using Cox regression and mixed ANCOVA models.

RESULTS

Participants were classified according to FDG-PET pattern in PD with typical (n = 26) and atypical cortical metabolism (n = 23). Patients with atypical brain hypometabolic patterns showed higher incidence of dementia (60% vs 3%; HR = 18.3), hallucinations (56% vs 7%, HR = 7.3) and faster motor decline compared to typical pattern group.

CONCLUSION

This study argues for specific patterns of FDG-PET cortical hypometabolism in PD as a prognostic marker for long term cognitive and motor outcomes at single-subject level.

Basal Forebrain Atrophy, Cortical Thinning, and Amyloid-β Status in Parkinson's disease-Related Cognitive Decline

BACKGROUND

Degeneration of the cortically-projecting cholinergic basal forebrain (cBF) is a well-established pathologic correlate of cognitive decline in Parkinson's disease (PD). In Alzheimer's disease (AD) the effect of cBF degeneration on cognitive decline was found to be mediated by parallel atrophy of denervated cortical areas.

OBJECTIVES

To examine whether the association between cBF degeneration and cognitive decline in PD is mediated by parallel atrophy of cortical areas and whether these associations depend on the presence of comorbid AD pathology.

METHODS

We studied 162 de novo PD patients who underwent serial 3 T magnetic resonance imaging scanning (follow-up: 2.33 ± 1.46 years) within the Parkinson's Progression Markers Initiative. cBF volume and regional cortical thickness were automatically calculated using established procedures. Individual slopes of structural brain changes and cognitive decline were estimated using linear-mixed models. Associations between longitudinal cBF degeneration, regional cortical thinning, and cognitive decline were assessed using regression analyses and mediation effects were assessed using nonparametric bootstrap. Complementary analyses assessed the effect of amyloid-β biomarker positivity on these associations.

RESULTS

After controlling for global brain atrophy, longitudinal cBF degeneration was highly correlated with faster cortical thinning (PFDR  < 0.05), and thinning in cBF-associated cortical areas mediated the association between cBF degeneration and cognitive decline (rcBF-MoCA  = 0.30, P < 0.001). Interestingly, both longitudinal cBF degeneration and its association with cortical thinning were largely independent of amyloid-β status.

CONCLUSIONS

cBF degeneration in PD is linked to parallel thinning of cortical target areas, which mediate the effect on cognitive decline. These associations are independent of amyloid-β status, indicating that they reflect proper features of PD pathophysiology. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Development and validation of a nomogram-assisted tool to predict potentially reversible cognitive frailty in Chinese community-living older adults

BACKGROUND

Cognitive frailty (CF) is a complex and heterogeneous clinical syndrome that indicates the onset of neurodegenerative processes and poor prognosis. In order to prevent the occurrence and development of CF in real world, we intended to develop and validate a simple and timely diagnostic instrument based on comprehensive geriatric assessment that will identify patients with potentially reversible CF (PRCF).

METHODS

750 community-dwelling individuals aged over 60 years were randomly allocated to either a training or validation set at a 4:1 ratio. We used the operator regression model offering the least absolute data dimension shrinkage and feature selection among candidate predictors. PRCF was defined as the presence of physical pre-frailty, frailty, and mild cognitive impairment (MCI) occurring simultaneously. Multivariate logistic regression was conducted to build a diagnostic tool to present data as a nomogram. The performance of the tool was assessed with respect to its calibration, discrimination, and clinical usefulness.

RESULTS

PRCF was observed in 326 patients (43%). Predictors in the tool were educational background, coronary heart disease, handgrip strength, gait speed, instrumental activity of daily living (IADL) disability, subjective cognitive decline (SCD) and five-times-sit-to-stand test. The diagnostic nomogram-assisted tool exhibited good calibration and discrimination with a C-index of 0.805 and a higher C-index of 0.845 in internal validation. The calibration plots demonstrated strong agreement in both the training and validation sets, while decision curve analysis confirmed the nomogram's efficacy in clinical practice.

CONCLUSIONS

This tool can effectively identify older adults at high risk for PRCF, enabling physicians to make informed clinical decisions and implement proper patient-centered individual interventions.

Tau: a biomarker of Huntington's disease

Developing effective treatments for patients with Huntington's disease (HD)-a neurodegenerative disorder characterized by severe cognitive, motor and psychiatric impairments-is proving extremely challenging. While the monogenic nature of this condition enables to identify individuals at risk, robust biomarkers would still be extremely valuable to help diagnose disease onset and progression, and especially to confirm treatment efficacy. If measurements of cerebrospinal fluid neurofilament levels, for example, have demonstrated use in recent clinical trials, other proteins may prove equal, if not greater, relevance as biomarkers. In fact, proteins such as tau could specifically be used to detect/predict cognitive affectations. We have herein reviewed the literature pertaining to the association between tau levels and cognitive states, zooming in on Alzheimer's disease, Parkinson's disease and traumatic brain injury in which imaging, cerebrospinal fluid, and blood samples have been interrogated or used to unveil a strong association between tau and cognition. Collectively, these areas of research have accrued compelling evidence to suggest tau-related measurements as both diagnostic and prognostic tools for clinical practice. The abundance of information retrieved in this niche of study has laid the groundwork for further understanding whether tau-related biomarkers may be applied to HD and guide future investigations to better understand and treat this disease.

Multifactorial assessment of Parkinson's disease course and outcomes using trajectory modeling in a multiethnic, multisite cohort - extension of the LONG-PD study

Background

The severity, progression, and outcomes of motor and non-motor symptoms in Parkinson's disease (PD) are quite variable. Following PD cohorts holds promise for identifying predictors of disease severity and progression.

Methods

PD patients (N = 871) were enrolled at five sites. Enrollment occurred within 5 years of initial motor symptom onset. Disease progression was assessed annually for 2-to-10 years after onset. Group-based trajectory modeling was used to identify groups differing in disease progression. Models were developed for UPDRS-III scores, UPDRS-III tremor and bradykinesia-rigidity subscores, Hoehn & Yahr (H&Y) stage, Mini-Mental Status Exam (MMSE) scores, and UPDRS-III, H&Y and MMSE scores considered together. Predictors of trajectory-group membership were modeled simultaneously with the trajectories. Kaplan-Meier survival analysis evaluated survival free of PD outcomes.

Results

The best fitting models identified three groups. One showed a relatively benign, slowly progressing trajectory (Group 1), a second showed a moderate, intermediately progressing trajectory (Group 2), and a third showed a more severe, rapidly progressing trajectory (Group 3). Stable trajectory-group membership occurred relatively early in the disease course, 5 years after initial motor symptom. Predictors of intermediate and more severe trajectory-group membership varied across the single variable models and the multivariable model jointly considering UPDRS-III, H&Y and MMSE scores. In the multivariable model, membership in Group 2 (28.4% of patients), relative to Group 1 (50.5%), was associated with male sex, younger age-at-onset, fewer education-years, pesticide exposure, absence of reported head injury, and akinetic/rigid subtype at initial presentation. Membership in Group 3 (21.3%), relative to Group 1, was associated with older age-at-onset, fewer education-years, pesticide exposure, and the absence of a tremor-predominant subtype at initial presentation. Persistent freezing, persistent falls, and cognitive impairment occurred earliest and more frequently in Group 3, later and less frequently in Group 2, and latest and least frequently in Group 1. Furthermore, autonomic complications, dysphagia, and psychosis occurred more frequently in Groups 2 and 3 than in Group 1.

Conclusion

Modeling disease course using multiple objective assessments over an extended follow-up duration identified groups that more accurately reflect differences in PD course, prognosis, and outcomes than assessing single parameters over shorter intervals.

Visual dysfunction is a better predictor than retinal thickness for dementia in Parkinson's disease

BACKGROUND

Dementia is a common and devastating symptom of Parkinson's disease (PD). Visual function and retinal structure are both emerging as potentially predictive for dementia in Parkinson's but lack longitudinal evidence.

METHODS

We prospectively examined higher order vision (skew tolerance and biological motion) and retinal thickness (spectral domain optical coherence tomography) in 100 people with PD and 29 controls, with longitudinal cognitive assessments at baseline, 18 months and 36 months. We examined whether visual and retinal baseline measures predicted longitudinal cognitive scores using linear mixed effects models and whether they predicted onset of dementia, death and frailty using time-to-outcome methods.

RESULTS

Patients with PD with poorer baseline visual performance scored lower on a composite cognitive score (β=0.178, SE=0.05, p=0.0005) and showed greater decreases in cognition over time (β=0.024, SE=0.001, p=0.013). Poorer visual performance also predicted greater probability of dementia (χ² (1)=5.2, p=0.022) and poor outcomes (χ² (1) =10.0, p=0.002). Baseline retinal thickness of the ganglion cell-inner plexiform layer did not predict cognitive scores or change in cognition with time in PD (β=-0.013, SE=0.080, p=0.87; β=0.024, SE=0.001, p=0.12).

CONCLUSIONS

In our deeply phenotyped longitudinal cohort, visual dysfunction predicted dementia and poor outcomes in PD. Conversely, retinal thickness had less power to predict dementia. This supports mechanistic models for Parkinson's dementia progression with onset in cortical structures and shows potential for visual tests to enable stratification for clinical trials.

Neurofilament light predicts worse nonmotor symptoms and depression in Parkinson's disease

BACKGROUND

The identification of biomarkers that reflect worse progression of nonmotor symptoms (NMS) in Parkinson's disease (PD) is currently an unmet need. The main aim of this study was to investigate whether cerebrospinal fluid (CSF) and serum neurofilament light (NfL), measured at baseline or longitudinally, can be used to predict the progression of NMS in patients with PD.

METHODS

Baseline and longitudinal NfL levels were measured in the CSF and serum in 392 PD patients and 184 healthy controls from the Parkinson's Progression Marker Initiative. NMS were assessed using several scales, including, but not restricted to, the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part I, the Geriatric Depression Scale (GDS) and the State-Trait Anxiety Inventory (STAI). The relationship between baseline and longitudinal NfL levels with changes in NMS was assessed using linear mixed effects models (LME) in PD patients. In addition, we compared CSF and serum NfL levels between groups and assessed the relationship between NfL biomarkers with baseline NMS. Finally, to assess the specificity of our findings we ran the previous LME models using other biomarkers such as CSF amyloid-β1-42, total tau, phosphorylated tau181 and total α-synuclein and we also ran the models in healthy controls.

RESULTS

Baseline levels and longitudinal changes in serum and CSF NfL predicted worse longitudinal MDS-UPDRS-I and depression scores over time in PD (p < 0.01). This relationship remained significant only for CSF NfL when controlling for motor and cognitive status. Furthermore, longitudinal changes in serum and CSF NfL were associated with worse anxiety over time in PD patients (p < 0.05). In contrast to CSF NfL, serum NfL levels were slightly higher at baseline (p = 0.043) and showed significant longitudinal increases (p < 0.001) in PD patients compared to controls. There were no significant correlations between NfL levels (CSF or serum) with other NMS scales, baseline NMS variables, other biomarkers or in healthy controls.

CONCLUSIONS

Our findings indicate that both serum and CSF NfL are associated with worse longitudinal NMS burden, particularly in relation to the progression of depression and anxiety. Serum NfL showed stronger associations with NMS suggesting it could potentially be used as a non-invasive marker of NMS progression for PD.

Structural brain differences in essential tremor and Parkinson's disease deep brain stimulation patients

BACKGROUND

Essential tremor (ET) and Parkinson's disease (PD) are the most common tremor disorders and are common indications for deep brain stimulation (DBS). In some patients, PD and ET symptoms overlap and diagnosis can be challenging based on clinical criteria alone. The objective of this study was to identify structural brain differences between PD and ET DBS patients to help differentiate these disorders and improve our understanding of the different brain regions involved in these pathologic processes.

METHODS

We included ET and PD patients scheduled to undergo DBS surgery in this observational study. Patients underwent 3T brain MRI while under general anesthesia as part of their procedure. Cortical thicknesses and subcortical volumes were quantified from T1-weighted images using automated multi-atlas segmentation. We used logistic regression analysis to identify brain regions associated with diagnosis of ET or PD.

RESULTS

149 ET and 265 PD patients were included. Smaller volumes in the pallidum and thalamus and reduced thickness in the anterior orbital gyrus, lateral orbital gyrus, and medial precentral gyrus were associated with greater odds of ET diagnosis. Conversely, reduced volumes in the caudate, amygdala, putamen, and basal forebrain, and reduced thickness in the orbital part of the inferior frontal gyrus, supramarginal gyrus, and posterior cingulate were associated with greater odds of PD diagnosis.

CONCLUSIONS

These findings identify structural brain differences between PD and ET patients. These results expand our understanding of the different brain regions involved in these disorders and suggest that structural MRI may help to differentiate patients with these two disorders.

Altered sleep and neurovascular dysfunction in alpha-synucleinopathies: the perfect storm for glymphatic failure

Clinical and cognitive progression in alpha-synucleinopathies is highly heterogeneous. While some patients remain stable over long periods of time, other suffer early dementia or fast motor deterioration. Sleep disturbances and nocturnal blood pressure abnormalities have been identified as independent risk factors for clinical progression but a mechanistic explanation linking both aspects is lacking. We hypothesize that impaired glymphatic system might play a key role on clinical progression. Glymphatic system clears brain waste during specific sleep stages, being blood pressure the motive force that propels the interstitial fluid through brain tissue to remove protein waste. Thus, the combination of severe sleep alterations, such as REM sleep behavioral disorder, and lack of the physiological nocturnal decrease of blood pressure due to severe dysautonomia may constitute the perfect storm for glymphatic failure, causing increased abnormal protein aggregation and spreading. In Lewy body disorders (Parkinson's disease and dementia with Lewy bodies) the increment of intraneuronal alpha-synuclein and extracellular amyloid-β would lead to cognitive deterioration, while in multisystemic atrophy, increased pathology in oligodendroglia would relate to the faster and malignant motor progression. We present a research model that may help in developing studies aiming to elucidate the role of glymphatic function and associated factors mainly in alpha-synucleinopathies, but that could be relevant also for other protein accumulation-related neurodegenerative diseases. If the model is proven to be useful could open new lines for treatments targeting glymphatic function (for example through control of nocturnal blood pressure) with the objective to ameliorate cognitive and motor progression in alpha-synucleinopathies.

Current coffee consumption is associated with decreased striatal dopamine transporter availability in Parkinson's disease patients and healthy controls

BACKGROUND

Coffee is the most widely consumed psychostimulant worldwide. Emerging evidence indicates that coffee consumption habit significantly reduces the risk of developing Parkinson's disease (PD). However, the effect of coffee consumption on nigrostriatal dopaminergic neurodegeneration is still largely unknown. We therefore aim to investigate the role of coffee consumption in nigrostriatal dopaminergic neurodegeneration using dopamine transporter (DAT) imaging in PD and healthy controls (HC).

METHODS

A total of 138 PD patients and 75 HC with questionnaires about coffee consumption, and DAT scans were recruited from the Parkinson's Progression Markers Initiative cohort. Demographic, clinical, and striatal DAT characteristics were compared across subgroups of current, former, and never coffee consumers in PD and HC, respectively. Furthermore, partial correlation analyses were performed to determine whether there was a relationship between coffee cups consumed per day and striatal DAT characteristics in each striatal region. In addition, the factors that may have influenced the loss of nigrostriatal dopaminergic neurons were included in multiple linear regression analyses to identify significant contributing factors to DAT availability in each striatal region.

RESULTS

PD patients had lower DAT availability in each striatal region than HC (p < 0.001). In PD patients, there were significant differences in DAT availability in the caudate (p = 0.008, Bonferroni corrected) across three PD subgroups. Specifically, post hoc tests showed that current coffee consumers had significantly lower DAT availability in the caudate than former coffee consumers (p = 0.01) and never coffee consumers (p = 0.022). In HC, there were significant differences in DAT availability in the caudate (p = 0.031, Bonferroni uncorrected) across three HC subgroups. Specifically, post hoc tests showed that current coffee consumers had significantly lower DAT availability in the caudate than former coffee consumers (p = 0.022). Moreover, correlation analysis revealed that cups per day were negatively correlated with DAT availability in the caudate in current consumers of PD patients (r =  - 0.219, p = 0.047). In addition, multiple linear regression analyses showed that current coffee consumption remained an independent predictor of decreased DAT availability in the caudate in PD patients and HC.

CONCLUSIONS

This study demonstrates that current coffee consumption is associated with decreased striatal DAT availability in the caudate. However, the effects of caffeine on striatal DAT may fade and disappear after quitting coffee consumption.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT01141023.

Cerebrospinal fluid GFAP is a predictive biomarker for conversion to dementia and Alzheimer's disease-associated biomarkers alterations among de novo Parkinson's disease patients: a prospective cohort study

BACKGROUND

Dementia is a prevalent non-motor manifestation among individuals with advanced Parkinson's disease (PD). Glial fibrillary acidic protein (GFAP) is an inflammatory marker derived from astrocytes. Research has demonstrated the potential of plasma GFAP to forecast the progression to dementia in PD patients with mild cognitive impairment (PD-MCI). However, the predictive role of cerebrospinal fluid (CSF) GFAP on future cognitive transformation and alterations in Alzheimer's disease (AD)-associated CSF biomarkers in newly diagnosed PD patients has not been investigated.

METHODS

210 de novo PD patients from the Parkinson's Progression Markers Initiative were recruited. Cognitive progression in PD participants was evaluated using Cox regression. Cross-sectional and longitudinal associations between baseline CSF GFAP and cognitive function and AD-related CSF biomarkers were evaluated using multiple linear regression and generalized linear mixed model.

RESULTS

At baseline, the mean age of PD participants was 60.85 ± 9.78 years, including 142 patients with normal cognition (PD-NC) and 68 PD-MCI patients. The average follow-up time was 6.42 ± 1.69 years. A positive correlation was observed between baseline CSF GFAP and age (β = 0.918, p < 0.001). There was no statistically significant difference in baseline CSF GFAP levels between PD-NC and PD-MCI groups. Higher baseline CSF GFAP predicted greater global cognitive decline over time in early PD patients (Montreal Cognitive Assessment, β = - 0.013, p = 0.014). Furthermore, Cox regression showed that high baseline CSF GFAP levels were associated with a high risk of developing dementia over an 8-year period in the PD-NC group (adjusted HR = 3.070, 95% CI 1.119-8.418, p = 0.029). In addition, the baseline CSF GFAP was positively correlated with the longitudinal changes of not only CSF α-synuclein (β = 0.313, p < 0.001), but also CSF biomarkers associated with AD, namely, amyloid-β 42 (β = 0.147, p = 0.034), total tau (β = 0.337, p < 0.001) and phosphorylated tau (β = 0.408, p < 0.001).

CONCLUSIONS

CSF GFAP may be a valuable prognostic tool that can predict the severity and progression of cognitive deterioration, accompanied with longitudinal changes in AD-associated pathological markers in early PD.

Clinical trials for cognition in Parkinson's disease: Where are we and how can we do better?

BACKGROUND

Cognitive impairment is common in Parkinson's disease (PD) and has a substantial impact on quality of life. Despite numerous trials targeting various PD features, we still lack effective treatments for cognition beyond cholinesterase inhibitors.

OBJECTIVE

To identify the gaps in recent clinical trials with cognitive outcomes in PD and consider areas for improvement.

METHODS

We examined recent clinical trials with cognitive outcomes in PD registered on ClinicalTrials.gov, excluding trials without cognitive outcomes, non-interventional studies, and in atypical Parkinsonian disorders. Included trials were categorized by treatment approach (investigational medicinal product, behavioral, physical activity, device-based). Details of trial design and outcomes were collected.

RESULTS

178 trials at different stages of trial completion were considered. 46 trials were completed, 25 had available results. Mean follow-up duration was 29.9 weeks. Most common cognitive measure was Montreal Cognitive Assessment. Most were performed in North America or Europe. Majority of the participants identified as non-Hispanic and White. Only eight trials showed improvement in cognition, none showed improvement beyond four months. These included trials of international medicinal products, cognitive and physical interventions and devices. GRADE certainty levels ranged from Moderate to Very Low. Only mevidalen had a Moderate certainty for potential clinical effectiveness.

CONCLUSIONS

Amongst a large number of trials for cognition in PD, only a small proportion were completed. Few showed significant improvement, with no proven long-lasting effects. Trial design, lack of enrichment for at-risk groups, short follow-up duration, insensitive outcome measures likely contribute to lack of detectable benefit and should be considered in future trials.

Identifying key multi-modal predictors of incipient dementia in Parkinson's disease: a machine learning analysis and Tree SHAP interpretation

Background

Persons with Parkinson's disease (PD) differentially progress to cognitive impairment and dementia. With a 3-year longitudinal sample of initially non-demented PD patients measured on multiple dementia risk factors, we demonstrate that machine learning classifier algorithms can be combined with explainable artificial intelligence methods to identify and interpret leading predictors that discriminate those who later converted to dementia from those who did not.

Method

Participants were 48 well-characterized PD patients (M_{baseline age} = 71.6; SD = 4.8; 44% female). We tested 38 multi-modal predictors from 10 domains (e.g., motor, cognitive) in a computationally competitive context to identify those that best discriminated two unobserved baseline groups, PD No Dementia (PDND), and PD Incipient Dementia (PDID). We used Random Forest (RF) classifier models for the discrimination goal and Tree SHapley Additive exPlanation (Tree SHAP) values for deep interpretation.

Results

An excellent RF model discriminated baseline PDID from PDND (AUC = 0.84; normalized Matthews Correlation Coefficient = 0.76). Tree SHAP showed that ten leading predictors of PDID accounted for 62.5% of the model, as well as their relative importance, direction, and magnitude (risk threshold). These predictors represented the motor (e.g., poorer gait), cognitive (e.g., slower Trail A), molecular (up-regulated metabolite panel), demographic (age), imaging (ventricular volume), and lifestyle (activities of daily living) domains.

Conclusion

Our data-driven protocol integrated RF classifier models and Tree SHAP applications to selectively identify and interpret early dementia risk factors in a well-characterized sample of initially non-demented persons with PD. Results indicate that leading dementia predictors derive from multiple complementary risk domains.