Application of the Updated Movement Disorder Society Criteria for Prodromal Parkinson's Disease to a Population-Based 10-Year Study

A top-down proteomic approach reveals a salivary protein profile able to classify Parkinson's disease with respect to Alzheimer's disease patients and to healthy controls

Parkinson's disease (PD) is a complex neurodegenerative disease with motor and non-motor symptoms. Diagnosis is complicated by lack of reliable biomarkers. To individuate peptides and/or proteins with diagnostic potential for early diagnosis, severity and discrimination from similar pathologies, the salivary proteome in 36 PD patients was investigated in comparison with 36 healthy controls (HC) and 35 Alzheimer's disease (AD) patients. A top-down platform based on HPLC-ESI-IT-MS allowed characterizing and quantifying intact peptides, small proteins and their PTMs (overall 51). The three groups showed significantly different protein profiles, PD showed the highest levels of cystatin SA and antileukoproteinase and the lowest of cystatin SN and some statherin proteoforms. HC exhibited the lowest abundance of thymosin β4, short S100A9, cystatin A, and dimeric cystatin B. AD patients showed the highest abundance of α-defensins and short oxidized S100A9. Moreover, different proteoforms of the same protein, as S-cysteinylated and S-glutathionylated cystatin B, showed opposite trends in the two pathological groups. Statherin, cystatins SA and SN classified accurately PD from HC and AD subjects. α-defensins, histatin 1, oxidized S100A9, and P-B fragments were the best classifying factors between PD and AD patients. Interestingly statherin and thymosin β4 correlated with defective olfactory functions in PD patients. All these outcomes highlighted implications of specific proteoforms involved in the innate-immune response and inflammation regulation at oral and systemic level, suggesting a possible panel of molecular and clinical markers suitable to recognize subjects affected by PD.

The epidemiology of Parkinson's disease

The epidemiology of Parkinson's disease shows marked variations in time, geography, ethnicity, age, and sex. Internationally, prevalence has increased over and above demographic changes. There are several potential reasons for this increase, including the decline in other competing causes of death. Whether incidence is increasing, especially in women or in many low-income and middle-income countries where there is a shortage of high-quality data, is less certain. Parkinson's disease is more common in older people and men, and a variety of environmental factors have been suggested to explain why, including exposure to neurotoxic agents. Within countries, there appear to be ethnic differences in disease risk, although these differences might reflect differential access to health care. The cause of Parkinson's disease is multifactorial, and involves genetic and environmental factors. Both risk factors (eg, pesticides) and protective factors (eg, physical activity and tendency to smoke) have been postulated to have a role in Parkinson's disease, although elucidating causality is complicated by the long prodromal period. Following the establishment of public health strategies to prevent cardiovascular diseases and some cancers, chronic neurodegenerative diseases such as Parkinson's disease and dementia are gaining a deserved higher priority. Multipronged prevention strategies are required that tackle population-based primary prevention, high-risk targeted secondary prevention, and Parkinson's disease-modifying therapies for tertiary prevention. Future international collaborations will be required to triangulate evidence from basic, applied, and epidemiological research, thereby enhancing the understanding and prevention of Parkinson's disease at a global level.

Comparison of different risk scores for Parkinson disease in a population-based 10-year study

BACKGROUND AND PURPOSE

Different algorithms aiming to identify individuals at risk of Parkinson disease (PD) have been proposed. Comparative studies of these scores and their recent updates in the general elder population are needed.

METHODS

We have previously applied the "basic" PREDICT-PD algorithm, designed for remote screening, and the original and updated Movement Disorder Society (MDS) criteria for prodromal PD to the longitudinal population-based Bruneck study cohort. We have now additionally employed the "enhanced" PREDICT-PD algorithm (which includes motor assessment, olfaction, probable rapid eye movement sleep behaviour disorder status, pesticide exposure, and diabetes as additional factors). Risk scores were calculated based on comprehensive baseline assessments (2005) in 574 subjects aged 55-94 years (290 females), and cases of incident PD were identified at 5-year (n = 11) and 10-year follow-up (n = 9). We analysed the association of the different log-transformed risk scores with incident PD at follow-up (calculated per 1-SD unit change).

RESULTS

The enhanced PREDICT-PD algorithm was associated with incident PD over 10-years of follow-up, yielding higher odds for incident PD (odds ratio [OR] = 4.61, 95% confidence interval [CI] = 2.68-7.93, p < 0.001) compared with the basic PREDICT-PD score (OR = 2.38, 95% CI = 1.49-3.79, p < 0.001). The updated MDS prodromal criteria yielded a numerically higher OR of 7.13 (95% CI = 3.49-14.54, p < 0.001) in comparison with the original criteria as well as the enhanced PREDICT-PD algorithm, with overlapping 95% CIs.

CONCLUSIONS

The enhanced PREDICT-PD algorithm was significantly associated with incident PD. The consistent performance of both the enhanced PREDICT-PD algorithm and the updated MDS prodromal criteria compared to their original versions supports their use in PD risk screening.

Dietary supplementation of Acanthopanax senticosus extract alleviates motor deficits in MPTP-induced Parkinson's disease mice and its underlying mechanism

Acanthopanax senticosus extract (ASE), a dietary supplement with antifatigue, neuroprotective, and immunomodulatory properties, has been widely used due to its high polyphenol content. Our previous study showed that ASE could be used to treat Parkinson's disease (PD) as it contains multiple monoamine oxidase B inhibitors prescribed in early PD. However, its mechanism remains ambiguous. In this study, we investigated the protective effects of ASE on MPTP-induced PD in mice and explored the underlying mechanisms of action. We found that the administration of ASE significantly improved motor coordination in mice with MPTP-induced PD. As shown by quantitative proteomic analysis, 128 proteins' expression significantly changed in response to ASE administration, most of which were involved with Fcγ receptor-mediated phagocytosis in macrophages and monocytes signaling pathway, PI3K/AKT signaling pathway, and insulin receptor signaling pathway. Furthermore, the network analysis results showed that ASE modulates protein networks involved in regulating cellular assembly, lipid metabolism, and morphogenesis, all of which have implications for treating PD. Overall, ASE served as a potential therapeutic because it regulated multiple targets to improve motor deficits, which could lay the strong foundation for developing anti-PD dietary supplements.

Identification of a possible proteomic biomarker in Parkinson's disease: discovery and replication in blood, brain and cerebrospinal fluid

Biomarkers to aid diagnosis and delineate the progression of Parkinson's disease are vital for targeting treatment in the early phases of the disease. Here, we aim to discover a multi-protein panel representative of Parkinson's and make mechanistic inferences from protein expression profiles within the broader objective of finding novel biomarkers. We used aptamer-based technology (SomaLogic®) to measure proteins in 1599 serum samples, 85 cerebrospinal fluid samples and 37 brain tissue samples collected from two observational longitudinal cohorts (the Oxford Parkinson's Disease Centre and Tracking Parkinson's) and the Parkinson's Disease Brain Bank, respectively. Random forest machine learning was performed to discover new proteins related to disease status and generate multi-protein expression signatures with potential novel biomarkers. Differential regulation analysis and pathway analysis were performed to identify functional and mechanistic disease associations. The most consistent diagnostic classifier signature was tested across modalities [cerebrospinal fluid (area under curve) = 0.74, P = 0.0009; brain area under curve = 0.75, P = 0.006; serum area under curve = 0.66, P = 0.0002]. Focusing on serum samples and using only those with severe disease compared with controls increased the area under curve to 0.72 (P = 1.0 × 10^-4). In the validation data set, we showed that the same classifiers were significantly related to disease status (P < 0.001). Differential expression analysis and weighted gene correlation network analysis highlighted key proteins and pathways with known relationships to Parkinson's. Proteins from the complement and coagulation cascades suggest a disease relationship to immune response. The combined analytical approaches in a relatively large number of samples, across tissue types, with replication and validation, provide mechanistic insights into the disease as well as nominate a protein signature classifier that deserves further biomarker evaluation.

Selected autonomic signs and symptoms as risk markers for phenoconversion and functional dependence in prodromal Parkinson's disease

PURPOSE

To determine whether dysautonomia can stratify individuals with other prodromal markers of Parkinson's disease (PD) for risk of phenoconversion and functional decline, which may help identify subpopulations appropriate for experimental studies.

METHODS

Data were obtained from Parkinson's Progression Markers Initiative. Cohorts without PD but with at-risk features were included (hyposmia and/or rapid-eye-movement-sleep behavior disorder, LRRK2 gene mutation, GBA gene mutation). Dysautonomia measures included Scales-for-Outcomes-in-Parkinson's-Disease Autonomic (SCOPA-AUT), seven SCOPA-AUT subscales, and cardiovascular dysfunction (supine hypertension, low pulse pressure, neurogenic orthostatic hypotension). Outcome measures were phenoconversion and Schwab-and-England Activities-of-Daily-Living (SE-ADL) ≤ 70, which indicates functional dependence. Cox proportional-hazards regression was used to evaluate survival to phenoconversion/SE-ADL ≤ 70 for each dysautonomia measure. If a significant association was identified, a likelihood-ratio test was employed to evaluate whether a significant interaction existed between the measure and cohort. If so, regression analysis was repeated stratified by cohort.

RESULTS

Median follow-up was 30 months. On multivariable analysis, gastrointestinal and female sexual dysfunction subscales were associated with increased risk of phenoconversion, while the cardiovascular subscale and neurogenic orthostatic hypotension were associated with increased risk of SE-ADL ≤ 70; respective hazard ratios (95% confidence intervals) were 1.13 (1.01-1.27), 3.26 (1.39-7.61), 1.87 (1.16-2.99), 5.45 (1.40-21.25). Only the association between the cardiovascular subscale and SE-ADL ≤ 70 was modified by cohort.

CONCLUSIONS

Symptoms of gastrointestinal and female sexual dysfunction predict phenoconversion in individuals with other risk markers for PD, while signs and symptoms of cardiovascular dysfunction may be associated with functional decline.

Prodromal Parkinson's disease: hype or hope for disease-modification trials?

The ultimate goal in Parkinson's disease (PD) research remains the identification of treatments that are capable of slowing or even halting the progression of the disease. The failure of numerous past disease-modification trials in PD has been attributed to a variety of factors related not only to choosing wrong interventions, but also to using inadequate trial designs and target populations. In patients with clinically established PD, neuronal pathology may already have advanced too far to be modified by any intervention. Based on such reasoning, individuals in yet prediagnostic or prodromal disease stages, may provide a window of opportunity to test disease-modifying strategies. There is now sufficient evidence from prospective studies to define diagnostic criteria for prodromal PD and several approaches have been studied in observational cohorts. These include the use of PD-risk algorithms derived from multiple established risk factors for disease as well as follow-up of cohorts with single defined prodromal markers like hyposmia, rapid eye movement sleep behavior disorders, or PD gene carriers. In this review, we discuss recruitment strategies for disease-modification trials in various prodromal PD cohorts, as well as potential trial designs, required trial durations, and estimated sample sizes. We offer a concluding outlook on how the goal of implementing disease-modification trials in prodromal cohorts might be achieved in the future.

Predicting Parkinson's Disease and Its Pathology via Simple Clinical Variables

Background:

Parkinson’s disease (PD) is a chronic, disabling neurodegenerative disorder.

Objective:

To predict a future diagnosis of PD using questionnaires and simple non-invasive clinical tests.

Methods:

Participants in the prospective Kuakini Honolulu-Asia Aging Study (HAAS) were evaluated biannually between 1995–2017 by PD experts using standard diagnostic criteria. Autopsies were sought on all deaths. We input simple clinical and risk factor variables into an ensemble-tree based machine learning algorithm and derived models to predict the probability of developing PD. We also investigated relationships of predictive models and neuropathologic features such as nigral neuron density.

Results:

The study sample included 292 subjects, 25 of whom developed PD within 3 years and 41 by 5 years. 116 (46%) of 251 subjects not diagnosed with PD underwent autopsy. Light Gradient Boosting Machine modeling of 12 predictors correctly classified a high proportion of individuals who developed PD within 3 years (area under the curve (AUC) 0.82, 95%CI 0.76–0.89) or 5 years (AUC 0.77, 95%CI 0.71–0.84). A large proportion of controls who were misclassified as PD had Lewy pathology at autopsy, including 79%of those who died within 3 years. PD probability estimates correlated inversely with nigral neuron density and were strongest in autopsies conducted within 3 years of index date (r = –0.57, p < 0.01).

Conclusion:

Machine learning can identify persons likely to develop PD during the prodromal period using questionnaires and simple non-invasive tests. Correlation with neuropathology suggests that true model accuracy may be considerably higher than estimates based solely on clinical diagnosis.