Unraveling Pathophysiological Mechanisms of Parkinson's Disease: Contribution of CSF Biomarkers

CSF markers of neurodegeneration Alzheimer's and Lewy body pathology in isolated REM sleep behavior disorder

We investigated the biomarker profile of neurodegeneration, Alzheimer's and Lewy body pathology in the CSF of 148 polysomnography-confirmed patients with isolated REM sleep behavior disorder (IRBD), a condition that precedes Parkinson's disease (PD) and dementia with Lewy bodies (DLB). We assessed misfolded α-synuclein (AS) by RT-QuIC assay, amyloid-beta peptides (Aβ42 and Aβ40), phosphorylated tau (p-tau), and total tau (t-tau) by CLEIA and neurofilament light chain (NfL) by ELISA. We detected AS in 75.3% of patients, pathologically decreased Aβ42/Aβ40 ratio in 22.5%, increased p-tau in 15.5%, increased t-tau in 14.9%, and elevated NfL in 14.7%. After a mean follow-up of 2.48 ± 2.75 years, 47 (38.1%) patients developed PD (n = 24) or DLB (n = 23). At CSF collection, AS positivity [HR 4.05 (1.26-12.99), p = 0.019], mild cognitive impairment [3.86 (1.96-7.61), p < 0.001], and abnormal DAT-SPECT [2.31 (1.09-4.91), p < 0.030] were independent predictors of conversion to PD and DLB. Among the other CSF markers, only elevated p-tau/Aβ42 was predictive of conversion, although only to DLB and not as an independent variable. In IRBD, CSF AS assessment by RT-QuIC provides an added value in defining the risk of short-term conversion to PD and DLB independent of clinical and instrumental investigations. Positive Alzheimer's disease (AD) pathology markers and elevated NfL occur in a subgroup of patients, but p-tau/Aβ42 is the only marker that predicts short-term conversion to DLB. Longer follow-up is needed to assess if AD biomarkers predict the later development of PD and DLB in IRBD.

Exploring the interplay between metabolomics and genetics in Parkinson's disease: Insights from ongoing research and future avenues

Parkinson's disease (PD) is a widespread neurodegenerative disorder, whose complex aetiology remains under construction. While rare variants have been associated with the monogenic PD form, most PD cases are influenced by multiple genetic and environmental aspects. Nonetheless, the pathophysiological pathways and molecular networks involved in monogenic/idiopathic PD overlap, and genetic variants are decisive in elucidating the convergent underlying mechanisms of PD. In this scenario, metabolomics has furnished a dynamic and systematic picture of the synergy between the genetic background and environmental influences that impact PD, making it a valuable tool for investigating PD-related metabolic dysfunctions. In this review, we performed a brief overview of metabolomics current research in PD, focusing on significant metabolic alterations observed in idiopathic PD from different biofluids and strata and exploring how they relate to genetic factors associated with monogenic PD. Dysregulated amino acid metabolism, lipid metabolism, and oxidative stress are the critical metabolic pathways implicated in both genetic and idiopathic PD. By merging metabolomics and genetics data, it is possible to distinguish metabolic signatures of specific genetic backgrounds and to pinpoint subgroups of PD patients who could derive personalized therapeutic benefits. This approach holds great promise for advancing PD research and developing innovative, cost-effective treatments.

Role of novel endpoints and evaluations of response in Parkinson disease

With progress in our understanding of Parkinson disease (PD) and other neurodegenerative disorders, from clinical features to imaging, genetic, and molecular characterization comes the opportunity to refine and revise how we measure these diseases and what outcome measures are used as endpoints in clinical trials. While several rater-, patient-, and milestone-based outcomes for PD exist that may serve as clinical trial endpoints, there remains an unmet need for endpoints that are clinically meaningful, patient centric while also being more objective and quantitative, less susceptible to effects of symptomatic therapy (for disease-modification trials), and that can be measured over a short period and yet accurately represent longer-term outcomes. Several novel outcomes that may be used as endpoints in PD clinical trials are in development, including digital measures of signs and symptoms, as well a growing array of imaging and biospecimen biomarkers. This chapter provides an overview of the state of PD outcome measures as of 2022, including considerations for selection of clinical trial endpoints in PD, advantages and limitations of existing measures, and emerging potential novel endpoints.

A computational approach based on weighted gene co-expression network analysis for biomarkers analysis of Parkinson's disease and construction of diagnostic model

Background

Parkinson's disease (PD) is a common age-related chronic neurodegenerative disease. There is currently no affordable, effective, and less invasive test for PD diagnosis. Metabolite profiling in blood and blood-based gene transcripts is thought to be an ideal method for diagnosing PD.

Aim

In this study, the objective is to identify the potential diagnostic biomarkers of PD by analyzing microarray gene expression data of samples from PD patients.

Methods

A computational approach, namely, Weighted Gene Co-expression Network Analysis (WGCNA) was used to construct co-expression gene networks and identify the key modules that were highly correlated with PD from the GSE99039 dataset. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to identify the hub genes in the key modules with strong association with PD. The selected hub genes were then used to construct a diagnostic model based on logistic regression analysis, and the Receiver Operating Characteristic (ROC) curves were used to evaluate the efficacy of the model using the GSE99039 dataset. Finally, Reverse Transcription-Polymerase Chain Reaction (RT-PCR) was used to validate the hub genes.

Results

WGCNA identified two key modules associated with inflammation and immune response. Seven hub genes, LILRB1, LSP1, SIPA1, SLC15A3, MBOAT7, RNF24, and TLE3 were identified from the two modules and used to construct diagnostic models. ROC analysis showed that the diagnostic model had a good diagnostic performance for PD in the training and testing datasets. Results of the RT-PCR experiments showed that there were significant differences in the mRNA expression of LILRB1, LSP1, and MBOAT7 among the seven hub genes.

Conclusion

The 7-gene panel (LILRB1, LSP1, SIPA1, SLC15A3, MBOAT7, RNF24, and TLE3) will serve as a potential diagnostic signature for PD.

Update on CSF Biomarkers in Parkinson's Disease

Progress in developing disease-modifying therapies in Parkinson’s disease (PD) can only be achieved through reliable objective markers that help to identify subjects at risk. This includes an early and accurate diagnosis as well as continuous monitoring of disease progression and therapy response. Although PD diagnosis still relies mainly on clinical features, encouragingly, advances in biomarker discovery have been made. The cerebrospinal fluid (CSF) is a biofluid of particular interest to study biomarkers since it is closest to the brain structures and therefore could serve as an ideal source to reflect ongoing pathologic processes. According to the key pathophysiological mechanisms, the CSF status of α-synuclein species, markers of amyloid and tau pathology, neurofilament light chain, lysosomal enzymes and markers of neuroinflammation provide promising preliminary results as candidate biomarkers. Untargeted approaches in the field of metabolomics provide insights into novel and interconnected biological pathways. Markers based on genetic forms of PD can contribute to identifying subgroups suitable for gene-targeted treatment strategies that might also be transferable to sporadic PD. Further validation analyses in large PD cohort studies will identify the CSF biomarker or biomarker combinations with the best value for clinical and research purposes.

Perivascular space in Parkinson's disease: Association with CSF amyloid/tau and cognitive decline

OBJECTIVE

Whether perivascular space (PVS) visible on magnetic resonance imaging (MRI) represents glymphatic dysfunction and whether this imaging marker is pathologic in Parkinson's disease (PD) have been controversial. The objective was to determine whether PVS visible on MRI is independently associated with cognitive decline in patients with PD, and to test whether pathologic proteins in the CSF (such as Aβ₄₂) mediate the pathologic role of PVS.

METHODS

A total of 341 patients with Parkinson's disease from Parkinson's Progression Marker Initiative (PPMI) cohort was included in the present study. PVS in the basal ganglia (BG-PVS) and centrum semiovale were evaluated with a semiquantitative scale. Changes in the Montreal Cognitive Assessment (MoCA) score and the absolute MoCA score at the 3-year assessment were considered the main cognitive outcome. A multivariable linear regression model was used to test the association between PVS and cognitive decline. A mixed linear model and path analysis were used to test the interaction among PVS, CSF biomarkers and cognitive decline.

RESULTS

BG-PVS was associated with cognitive decline in patients with PD at the 3-year follow-up independent of age, baseline cognition, motor and nonmotor function, presynaptic dopaminergic deficiency, and CSF biomarkers. The interaction between BG-PVS and Aβ₄₂/tTau, Aβ₄₂/pTau, and Aβ₄₂ levels was significantly predictive of 3-year cognitive decline. Path analysis confirmed that CSF Aβ₄₂/tTau levels partially mediated the pathologic effect of BG-PVS on cognitive outcome in PD.

CONCLUSIONS

BG-PVS is independently associated with cognitive decline in PD, and this association may be partially mediated by toxic CSF proteins.

A simple-to-use web-based calculator for survival prediction in Parkinson's disease

Background: To establish and validate a nomogram and corresponding web-based calculator to predict the survival of patients with Parkinson’s disease (PD).

Methods: In this cohort study, we retrospectively evaluated patients (n=497) with PD using a two-stage design, from March 2004 to November 2007 and from July 2005 to July 2015. Predictive variables included in the model were identified by univariate and multiple Cox proportional hazard analyses in the training set.

Results: Independent prognostic factors including age, PD duration, and Hoehn and Yahr stage were determined and included in the model. The model showed good discrimination power with the area under the curve (AUC) values generated to predict 4-, 6-, and 8-year survival in the training set being 0.716, 0.783, and 0.814, respectively. In the validation set, the AUCs of 4- and 6-year survival predictions were 0.85 and 0.924, respectively. Calibration plots and decision curve analysis showed good model performance both in the training and validation sets. For convenient application, we established a web-based calculator (https://tangyl.shinyapps.io/PDprognosis/).

Conclusions: We developed a satisfactory, simple-to-use nomogram and corresponding web-based calculator based on three relevant factors to predict prognosis and survival of patients with PD. This model can aid personalized treatment and clinical decision-making.