PREDICT-PD: An online approach to prospectively identify risk indicators of Parkinson's disease

Web Application to Enable Online Social Interactions in a Parkinson Disease Risk Cohort: Feasibility Study and Social Network Analysis

BACKGROUND

There is evidence that social interaction has an inverse association with the development of neurodegenerative diseases. PREDICT-Parkinson Disease (PREDICT-PD) is an online UK cohort study that stratifies participants for risk of future Parkinson disease (PD).

OBJECTIVE

This study aims to explore the methodological approach and feasibility of assessing the digital social characteristics of people at risk of developing PD and their social capital within the PREDICT-PD platform, making hypotheses about the relationship between web-based social engagement and potential predictive risk indicators of PD.

METHODS

A web-based application was built to enable social interaction through the PREDICT-PD portal. Feedback from existing members of the cohort was sought and informed the design of the pilot. Dedicated staff used weekly engagement activities, consisting of PD-related research, facts, and queries, to stimulate discussion. Data were collected by the hosting platform. We examined the pattern of connections generated over time through the cumulative number of posts and replies and ego networks using social network analysis. We used network metrics to describe the bonding, bridging, and linking of social capital among participants on the platform. Relevant demographic data and Parkinson risk scores (expressed as an odd 1:x) were analyzed using descriptive statistics. Regression analysis was conducted to estimate the relationship between risk scores (after log transformation) and network measures.

RESULTS

Overall, 219 participants took part in a 4-month pilot forum embedded in the study website. In it, 200 people (n=80, 40% male and n=113, 57% female) connected in a large group, where most pairs of users could reach one another either directly or indirectly through other users. A total of 59% (20/34) of discussions were spontaneously started by participants. Participation was asynchronous, with some individuals acting as "brokers" between groups of discussions. As more participants joined the forum and connected to one another through online posts, distinct groups of connected users started to emerge. This pilot showed that a forum application within the cohort web platform was feasible and acceptable and fostered digital social interaction. Matching participants' web-based social engagement with previously collected data at individual level in the PREDICT-PD study was feasible, showing potential for future analyses correlating online network characteristics with the risk of PD over time, as well as testing digital social engagement as an intervention to modify the risk of developing neurodegenerative diseases.

CONCLUSIONS

The results from the pilot suggest that an online forum can serve as an intervention to enhance social connectedness and investigate whether patterns of online engagement can impact the risk of developing PD through long-term follow-up. This highlights the potential of leveraging online platforms to study the role of social capital in moderating PD risk and underscores the feasibility of such approaches in future research or interventions.

The rise of Parkinson's disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and "eat, move, sleep" lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand

The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.

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.

Parkinson Disease Recognition Using a Gamified Website: Machine Learning Development and Usability Study

BACKGROUND

Parkinson disease (PD) affects millions globally, causing motor function impairments. Early detection is vital, and diverse data sources aid diagnosis. We focus on lower arm movements during keyboard and trackpad or touchscreen interactions, which serve as reliable indicators of PD. Previous works explore keyboard tapping and unstructured device monitoring; we attempt to further these works with structured tests taking into account 2D hand movement in addition to finger tapping. Our feasibility study uses keystroke and mouse movement data from a remotely conducted, structured, web-based test combined with self-reported PD status to create a predictive model for detecting the presence of PD.

OBJECTIVE

Analysis of finger tapping speed and accuracy through keyboard input and analysis of 2D hand movement through mouse input allowed differentiation between participants with and without PD. This comparative analysis enables us to establish clear distinctions between the two groups and explore the feasibility of using motor behavior to predict the presence of the disease.

METHODS

Participants were recruited via email by the Hawaii Parkinson Association (HPA) and directed to a web application for the tests. The 2023 HPA symposium was also used as a forum to recruit participants and spread information about our study. The application recorded participant demographics, including age, gender, and race, as well as PD status. We conducted a series of tests to assess finger tapping, using on-screen prompts to request key presses of constant and random keys. Response times, accuracy, and unintended movements resulting in accidental presses were recorded. Participants performed a hand movement test consisting of tracing straight and curved on-screen ribbons using a trackpad or mouse, allowing us to evaluate stability and precision of 2D hand movement. From this tracing, the test collected and stored insights concerning lower arm motor movement.

RESULTS

Our formative study included 31 participants, 18 without PD and 13 with PD, and analyzed their lower limb movement data collected from keyboards and computer mice. From the data set, we extracted 28 features and evaluated their significances using an extra tree classifier predictor. A random forest model was trained using the 6 most important features identified by the predictor. These selected features provided insights into precision and movement speed derived from keyboard tapping and mouse tracing tests. This final model achieved an average F1-score of 0.7311 (SD 0.1663) and an average accuracy of 0.7429 (SD 0.1400) over 20 runs for predicting the presence of PD.

CONCLUSIONS

This preliminary feasibility study suggests the possibility of using technology-based limb movement data to predict the presence of PD, demonstrating the practicality of implementing this approach in a cost-effective and accessible manner. In addition, this study demonstrates that structured mouse movement tests can be used in combination with finger tapping to detect PD.

The 8-item NHANES pocket smell test®: Normative data

This study provides normative data useful for interpreting scores from the Pocket Smell Test® (PST®), a brief "scratch & sniff" neuropsychological olfactory screening test comprised of 8 items from the 40-item University of Pennsylvania Smell Identification Test (UPSIT®). We combined 3,485 PST® scores from the 2013 to 2014 National Health and Nutrition Survey (NHANES) of persons 40 years of age and older with equivalent PST® items extracted from an UPSIT® database of 3,900 persons ranging in age from 5 to 99 years. Decade-related age- and gender-adjusted percentile normative data were established across the entire age spectrum. Cut-points for defining clinically useful categories of anosmia, probable microsmia, and normosmia were determined using receiver operating characteristic (ROC) curve analyses. An age-related decline in test scores was evident for both sexes after the age of 40 years, with women outperforming men. Based on the ROC analyses, subjects scoring 3 or less (AUC = 0.81) defines anosmia. Regardless of sex, a score of 7 or 8 on the N-PST® signifies normal function (AUC of 0.71). Probable microsmia is classified as scores extending from 3 to 6. These data provide an accurate means for interpreting PST® scores within a number of clinical and applied settings.

Challenges in the study of individuals at risk for Parkinson disease

Identifying individuals at high risk for developing neurodegenerative disease opens the possibility of conducting clinical trials that intervene at an earlier stage of neurodegeneration than has been possible to date, and in doing so hopefully improves the odds of efficacy for interventions aimed at slowing or stopping the disease process. The long prodromal phase of Parkinson disease presents opportunities and challenges to establishing cohorts of at-risk individuals. Recruiting people with genetic variants conferring increased risk and people with REM sleep behavior disorder currently constitutes the most promising strategies, but multistage screening of the general population may also be feasible capitalizing on known risk factors and prodromal features. This chapter discusses the challenges involved in identifying, recruiting, and retaining these individuals, and provides insights into possible solutions using examples from studies to date.

Cognitive impairment in REM-sleep behaviour disorder and individuals at risk of Parkinson's disease

BACKGROUND

Mild cognitive impairment (MCI) is commonly present at the time of Parkinson's Disease (PD) diagnosis, but its prevalence amongst individuals at increased risk of PD is unclear.

METHODS

Cognition was assessed using the Montreal Cognitive Assessment (MoCA) in 208 participants in the PREDICT-PD study, and 25 participants with REM-sleep behaviour disorder (RBD). Prevalence of MCI level I was determined in all participants, and level II MCI in the RBD sub-group.

RESULTS

Total MoCA scores were worse in the higher risk than the lower risk group defined as those below the 15th percentile of risk (p = 0.009), and in the RBD group compared to all healthy participants (p < 0.001). The prevalence of MCI level I was 12.8% in the lower-risk, 21.9% in the higher-risk (within the highest 15th percentile) and 64% in RBD participants; 66% of RBD participants had MCI level II with multi-domain MCI, but particularly attention and memory deficits.

CONCLUSIONS

Cognitive impairment is increased in different groups at higher risk of PD, particularly in the subgroup formally diagnosed with RBD.

Serum MicroRNAs Predict Isolated Rapid Eye Movement Sleep Behavior Disorder and Lewy Body Diseases

BACKGROUND

Isolated rapid eye movement sleep behavior disorder (IRBD) is a well-established clinical risk factor for Lewy body diseases (LBDs), such as Parkinson's disease (PD) and dementia with Lewy bodies (DLB).

OBJECTIVE

To elucidate whether serum microRNA (miRNA) deregulation in IRBD can antedate the diagnosis of LBD by performing a longitudinal study in different progression stages of IRBD before and after LBD diagnosis and assessing the predictive performance of differentially expressed miRNAs by machine learning-based modeling.

METHODS

Using genome-wide miRNA analysis and real-time quantitative polymerase chain reaction validation, we assessed serum miRNA profiles from patients with IRBD stratified by dopamine transporter (DaT) single-photon emission computed tomography into DaT-negative IRBD (n = 17) and DaT-positive IRBD (n = 21), IRBD phenoconverted into LBD (n = 13), and controls (n = 20). Longitudinally, we followed up the IRBD cohort by studying three time point serum samples over 26 months.

RESULTS

We found sustained cross-sectional and longitudinal deregulation of 12 miRNAs across the RBD continuum, including DaT-negative IRBD, DaT-positive IRBD, and LBD phenoconverted IRBD (let-7c-5p, miR-19b-3p, miR-140, miR-22-3p, miR-221-3p, miR-24-3p, miR-25-3p, miR-29c-3p, miR-361-5p, miR-425-5p, miR-4505, and miR-451a) (false discovery rate P < 0.05). Age- and sex-adjusted predictive modeling based on the 12 differentially expressed miRNA biosignatures discriminated IRBD and PD or DLB from controls with an area under the curve of 98% (95% confidence interval: 89-99%).

CONCLUSIONS

Besides clinical diagnosis of IRBD or imaging markers such as DaT single-photon emission computed tomography, specific miRNA biosignatures alone hold promise as progression biomarkers for patients with IRBD for predicting PD and DLB clinical outcomes. Further miRNA studies in other PD at-risk populations, such as LRRK2 mutation asymptomatic carriers or hyposmic subjects, are warranted. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

What have we learned from genome-wide association studies (GWAS) in Parkinson's disease?

After fifteen years of genome-wide association studies (GWAS) in Parkinson's disease (PD), what have we learned? Addressing this question will help catalogue the progress made towards elucidating disease mechanisms, improving the clinical utility of the identified loci, and envisioning how we can harness the strides to develop translational GWAS strategies. Here we review the advances of PD GWAS made to date while critically addressing the challenges and opportunities for next-generation GWAS. Thus, deciphering the missing heritability in underrepresented populations is currently at the reach of hand for a truly comprehensive understanding of the genetics of PD across the different ethnicities. Moreover, state-of-the-art GWAS designs hold a true potential for enhancing the clinical applicability of genetic findings, for instance, by improving disease prediction (PD risk and progression). Lastly, advanced PD GWAS findings, alone or in combination with clinical and environmental parameters, are expected to have the capacity for defining patient enriched cohorts stratified by genetic risk profiles and readily available for neuroprotective clinical trials. Overall, envisioning future strategies for advanced GWAS is currently timely and can be instrumental in providing novel genetic readouts essential for a true clinical translatability of PD genetic findings.

Cognition in prodromal Parkinson's disease

One characteristic of Parkinson's disease (PD) is a prodromal phase, lasting many years during which both pre-clinical motor and non-motor symptoms occur. Around one-fifth of patients with PD manifest mild cognitive impairment at time of clinical diagnosis. Thus, important challenges are to define the time of onset of cognitive dysfunction in the prodromal phase of PD, and to define its co-occurrence with other specific characteristics. Evidence for cognitive change in prodromal PD comes from various study designs, including both longitudinal and cross-sectional approaches with different target groups. These studies support the concept that changes in global cognitive function and alterations in executive functions occur, and that these changes may be present up to 6 years before clinical PD diagnosis. Notably, this evidence led to including global cognitive impairment as an independent prodromal marker in the recently updated research criteria of the Movement Disorder Society for prodromal PD. Knowledge in this field, however, is still at its beginning, and evidence is sparse about many aspects of this topic. Further longitudinal studies including standardized assessments of global and domain-specific cognitive functions are needed to gain further knowledge about the first appearance, the course, and the interaction of cognitive deficits with other non-motor symptoms in prodromal stage PD. Treatment approaches, including non-pharmacological interventions, in individuals with prodromal PD might help to prevent or delay cognitive dysfunction in early PD.

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.

The potential utility of smell testing to screen for neurodegenerative disorders

INTRODUCTION

Loss of smell is a common early feature of neurodegenerative diseases including Alzheimer's and Parkinson's disease. Identifying these conditions in their early stages is important to understand more about early pathophysiological events and the development of disease modifying therapies. Smell testing may be an effective future tool for screening large populations for early neurodegeneration.

AREAS COVERED

: In this review, we appraise the evidence for, and discuss the likelihood of, the use of smell testing in large screening programs to detect early neurodegeneration. We evaluate the predictive power of smell tests for neurodegenerative disease, compare performance to other established screening programs, and discuss ethical and practical considerations and limitations.

EXPERT OPINION

: Even if disease modifying therapies were available for neurodegenerative disease, smell tests alone are unlikely to have high enough predictive power to be used in a future screening program. However, we believe they could be a valuable component of a short battery of tests or part of a stepwise process that together could more accurately identify early neurodegeneration in large populations.

Optimising classification of Parkinson's disease based on motor, olfactory, neuropsychiatric and sleep features

Olfactory loss, motor impairment, anxiety/depression, and REM-sleep behaviour disorder (RBD) are prodromal Parkinson’s disease (PD) features. PD risk prediction models typically dichotomize test results and apply likelihood ratios (LRs) to scores above and below cut-offs. We investigate whether LRs for specific test values could enhance classification between PD and controls. PD patient data on smell (UPSIT), possible RBD (RBD Screening Questionnaire), and anxiety/depression (LADS) were taken from the Tracking Parkinson’s study (n = 1046). For motor impairment (BRAIN test) in PD cases, published data were supplemented (n = 87). Control data (HADS for anxiety/depression) were taken from the PREDICT-PD pilot study (n = 1314). UPSIT, RBDSQ, and anxiety/depression data were analysed using logistic regression to determine which items were associated with PD. Gaussian distributions were fitted to BRAIN test scores. LRs were calculated from logistic regression models or score distributions. False-positive rates (FPRs) for specified detection rates (DRs) were calculated. Sixteen odours were associated with PD; LRs for this set ranged from 0.005 to 5511. Six RBDSQ and seven anxiety/depression questions were associated with PD; LRs ranged from 0.35 to 69 and from 0.002 to 402, respectively. BRAIN test LRs ranged from 0.16 to 1311. For a 70% DR, the FPR was 2.4% for the 16 odours, 4.6% for anxiety/depression, 16.0% for the BRAIN test, and 20.0% for the RBDSQ. Specific selections of (prodromal) PD marker features rather than dichotomized marker test results optimize PD classification. Such optimized classification models could improve the ability of algorithms to detect prodromal PD; however, prospective studies are needed to investigate their value for PD-prediction models.

Parkinson's disease outside the brain: targeting the autonomic nervous system

Patients with Parkinson's disease present with signs and symptoms of dysregulation of the peripheral autonomic nervous system that can even precede motor deficits. This dysregulation might reflect early pathology and therefore could be targeted for the development of prodromal or diagnostic biomarkers. Only a few objective clinical tests assess disease progression and are used to evaluate the entire spectrum of autonomic dysregulation in patients with Parkinson's disease. However, results from epidemiological studies and findings from new animal models suggest that the dysfunctional autonomic nervous system is a probable route by which Parkinson's disease pathology can spread both to and from the CNS. The autonomic innervation of the gut, heart, and skin is affected by α-synuclein pathology in the early stages of the disease and might initiate α-synuclein spread via the autonomic connectome to the CNS. The development of easy-to-use and reliable clinical tests of autonomic nervous system function seems crucial for early diagnosis, and for developing strategies to stop or prevent neurodegeneration in Parkinson's disease.

Modulation of the Interactions Between α-Synuclein and Lipid Membranes by Post-translational Modifications

Parkinson's disease is characterised by the presence in brain tissue of aberrant inclusions known as Lewy bodies and Lewy neurites, which are deposits composed by α-synuclein and a variety of other cellular components, including in particular lipid membranes. The dysregulation of the balance between lipid homeostasis and α-synuclein homeostasis is therefore likely to be closely involved in the onset and progression of Parkinson's disease and related synucleinopathies. As our understanding of this balance is increasing, we describe recent advances in the characterisation of the role of post-translational modifications in modulating the interactions of α-synuclein with lipid membranes. We then discuss the impact of these advances on the development of novel diagnostic and therapeutic tools for synucleinopathies.

Prevalence of idiopathic REM behavior disorder: a systematic review and meta-analysis

STUDY OBJECTIVES

To provide an overall estimate of the prevalence of idiopathic REM Sleep Behavior Disorder (iRBD).

METHODS

Two investigators have independently searched the PubMed and Scopus databases for population-based studies assessing the prevalence of iRBD. Data about type of diagnosis (polysomnographic diagnosis, defined iRBD [dRBD]; clinical diagnosis, probable RBD [pRBD]), continent, age range of the screened population, quality of the studies, sample size, screening questionnaires, and strategies have been gathered. A random-effect model was used to estimate the pooled prevalence. Heterogeneity was investigated with subgroup analysis and meta-regression.

RESULTS

From 857 articles found in the databases, 19 articles were selected for the systematic review and meta-analysis. According to the type of diagnosis, five studies identified dRBD cases given a pooled prevalence of 0.68% (95% confidence interval [CI] 0.38-1.05) without significant heterogeneity (Cochran's Q p = 0.11; I2 = 46.43%). Fourteen studies assessed the prevalence of pRBD with a pooled estimate of 5.65% (95% CI 4.29-7.18) and a significant heterogeneity among the studies (Cochran's Q p < 0.001; I2 = 98.21%). At the subgroup analysis, significant differences in terms of prevalence were present according to the quality of the studies and, after removing two outlaying studies, according to the continents and the screening questionnaire used. Meta-regression did not identify any significant effect of the covariates on the pooled estimates.

CONCLUSION

Prevalence estimates of iRBD are significantly impacted by diagnostic level of certainty. Variations in pRBD prevalence are due to methodological differences in study design and screening questionnaires employed.

The Genetics of Parkinson's Disease and Implications for Clinical Practice

The genetic landscape of Parkinson’s disease (PD) is characterised by rare high penetrance pathogenic variants causing familial disease, genetic risk factor variants driving PD risk in a significant minority in PD cases and high frequency, low penetrance variants, which contribute a small increase of the risk of developing sporadic PD. This knowledge has the potential to have a major impact in the clinical care of people with PD. We summarise these genetic influences and discuss the implications for therapeutics and clinical trial design.

Challenges in the diagnosis of Parkinson's disease

Parkinson's disease is the second most common neurodegenerative disease and its prevalence has been projected to double over the next 30 years. An accurate diagnosis of Parkinson's disease remains challenging and the characterisation of the earliest stages of the disease is ongoing. Recent developments over the past 5 years include the validation of clinical diagnostic criteria, the introduction and testing of research criteria for prodromal Parkinson's disease, and the identification of genetic subtypes and a growing number of genetic variants associated with risk of Parkinson's disease. Substantial progress has been made in the development of diagnostic biomarkers, and genetic and imaging tests are already part of routine protocols in clinical practice, while novel tissue and fluid markers are under investigation. Parkinson's disease is evolving from a clinical to a biomarker-supported diagnostic entity, for which earlier identification is possible, different subtypes with diverse prognosis are recognised, and novel disease-modifying treatments are in development.

Olfactory-Trigeminal Interactions in Patients with Parkinson's Disease

Olfactory dysfunction (OD) is a highly frequent early non-motor symptom of Parkinson's disease (PD). An important step to potentially use OD for the development of early diagnostic tools of PD is to differentiate PD-related OD from other forms of non-parkinsonian OD (NPOD: postviral, sinunasal, post-traumatic, and idiopathic OD). Measuring non-olfactory chemosensory modalities, especially the trigeminal system, may allow to characterize a PD-specific olfactory profile. We here review the literature on PD-specific chemosensory alteration patterns compared with NPOD. Specifically, we focused on the impact of PD on the trigeminal system and particularly on the interaction between olfactory and trigeminal systems. As this interaction is seemingly affected in a disease-specific manner, we propose a model of interaction between both chemosensory systems that is distinct for PD-related OD and NPOD. These patterns of chemosensory impairment still need to be confirmed in prodromal PD; nevertheless, appropriate chemosensory tests may eventually help to develop diagnostic tools to identify individuals at risks for PD.

Improving estimation of Parkinson's disease risk-the enhanced PREDICT-PD algorithm

We previously reported a basic algorithm to identify the risk of Parkinson’s disease (PD) using published data on risk factors and prodromal features. Using this algorithm, the PREDICT-PD study identified individuals at increased risk of PD and used tapping speed, hyposmia and REM sleep behaviour disorder (RBD) as “intermediate” markers of prodromal PD in the absence of sufficient incident cases. We have now developed and tested an enhanced algorithm which incorporates the intermediate markers into the risk model. Risk estimates were compared using the enhanced and the basic algorithm in members of the PREDICT-PD pilot cohort. The enhanced PREDICT-PD algorithm yielded a much greater range of risk estimates than the basic algorithm (93–609-fold difference between the 10th and 90th centiles vs 10–13-fold respectively). There was a greater increase in the risk of PD with increasing risk scores for the enhanced algorithm than for the basic algorithm (hazard ratios per one standard deviation increase in log risk of 2.75 [95% CI 1.68–4.50; p < 0.001] versus 1.47 [95% CI 0.86–2.51; p = 0.16] respectively). Estimates from the enhanced algorithm also correlated more closely with subclinical striatal DaT-SPECT dopamine depletion (R² = 0.164, p = 0.005 vs R² = 0.043, p = 0.17). Incorporating the previous intermediate markers of prodromal PD and using likelihood ratios improved the accuracy of the PREDICT-PD prediction algorithm.

Towards subgroup-specific risk estimates: A meta-analysis of longitudinal studies on olfactory dysfunction and risk of Parkinson's disease

BACKGROUND

Interest has risen in identifying individuals at high risk of incident Parkinson's disease (PD) to facilitate inclusion in neuroprotective treatment trials. Current risk estimates of prodromal markers are based on aggregated data of an entire population, but this approach disregards differences in risk estimates by subgroups of a population. In this proof of concept, we determine subgroup-specific risk estimates of olfactory dysfunction for incident PD.

METHODS

PubMed, EMBASE and Cochrane were searched for prospective studies investigating the association between olfactory dysfunction and incident PD. Random-effects meta-analysis, subgroup analyses and meta-regression were performed to investigate general and subgroup risk estimates.

RESULTS

Individuals with odor identification dysfunction seemed to be at greater risk of incident PD compared to controls without olfactory dysfunction (OR = 4.18; 95%CI [2.47-7.07]). Risk estimates were higher in studies that included higher percentages of women (regression slope β = 0.053 increase in log odds ratio per 1% increase 1%, p = 0.0006), increased with mean study age (β = 0.21 per one year increase; p = 0.005) and in REM-sleep behavior disorder cohorts (β = 1.95; p = 0.03). Furthermore, the association between olfactory dysfunction and incident PD was most distinct in studies with shorter follow-up duration (ß = -0.56; p = 0.0047).

CONCLUSION

The presence of olfactory dysfunction conveys a considerably elevated risk of incident PD, likely more in studies with a higher proportion of women, older individuals or short follow-up duration. Individual patient data are warranted to confirm these findings and to yield subgroup-specific risk estimates of other common markers to refine prodromal PD criteria.

Video-Based Analyses of Parkinson's Disease Severity: A Brief Review

Remote and objective assessment of the motor symptoms of Parkinson's disease is an area of great interest particularly since the COVID-19 crisis emerged. In this paper, we focus on a) the challenges of assessing motor severity via videos and b) the use of emerging video-based Artificial Intelligence (AI)/Machine Learning techniques to quantitate human movement and its potential utility in assessing motor severity in patients with Parkinson's disease. While we conclude that video-based assessment may be an accessible and useful way of monitoring motor severity of Parkinson's disease, the potential of video-based AI to diagnose and quantify disease severity in the clinical context is dependent on research with large, diverse samples, and further validation using carefully considered performance standards.

Parkinson's disease determinants, prediction and gene-environment interactions in the UK Biobank

OBJECTIVE

To systematically investigate the association of environmental risk factors and prodromal features with incident Parkinson's disease (PD) diagnosis and the interaction of genetic risk with these factors. To evaluate whether existing risk prediction algorithms are improved by the inclusion of genetic risk scores.

METHODS

We identified individuals with an incident diagnosis of PD (n=1276) and controls (n=500 406) in UK Biobank. We determined the association of risk factors with incident PD using adjusted logistic regression models. We constructed polygenic risk scores (PRSs) using external weights and selected the best PRS from a subset of the cohort (30%). The PRS was used in a separate testing set (70%) to examine gene-environment interactions and compare predictive models for PD.

RESULTS

Strong evidence of association (false discovery rate <0.05) was found between PD and a positive family history of PD, a positive family history of dementia, non-smoking, low alcohol consumption, depression, daytime somnolence, epilepsy and earlier menarche. Individuals with the highest 10% of PRSs had increased risk of PD (OR 3.37, 95% CI 2.41 to 4.70) compared with the lowest risk decile. A higher PRS was associated with earlier age at PD diagnosis and inclusion of the PRS in the PREDICT-PD algorithm led to a modest improvement in model performance. We found evidence of an interaction between the PRS and diabetes.

INTERPRETATION

Here, we used UK Biobank data to reproduce several well-known associations with PD, to demonstrate the validity of a PRS and to demonstrate a novel gene-environment interaction, whereby the effect of diabetes on PD risk appears to depend on background genetic risk for PD.

Application of a Simple Parkinson's Disease Risk Score in a Longitudinal Population-Based Cohort

BACKGROUND

Identifying individuals at risk of developing Parkinson's disease (PD) is critical to define target populations for future neuroprotective trials.

OBJECTIVE

The objective of this study was to apply the PREDICT-PD algorithm of risk indicators for PD in a prospective community-based study (the Bruneck study), representative of the general elderly population.

METHODS

PREDICT-PD risk scores were calculated based on risk factor assessments obtained at baseline (2005, n = 574 participants). Cases of incident PD were identified at 5-year and 10-year follow-ups. Participants with PD or secondary parkinsonism at baseline were excluded (n = 35). We analyzed the association of log-transformed risk scores with the presence of well-established markers as surrogates for PD risk at baseline and with incident PD at follow-up.

RESULTS

A total of 20 participants with incident PD were identified during follow-up (11 after 5 years and 9 after 10 years). Baseline PREDICT-PD risk scores were associated with incident PD with odds ratios of 2.09 (95% confidence interval, 1.35-3.25; P = 0.001) after 5 years and of 1.95 (1.36-2.79; P < 0.001) after 10 years of follow-up per doubling of risk scores. In addition, higher PREDICT-PD scores were significantly correlated with established PD risk markers (olfactory dysfunction, signs of rapid eye movement sleep behavior disorder and motor deficits) and significantly associated with higher probability for prodromal PD according to the Movement Disorder Society research criteria at baseline.

CONCLUSIONS

The PREDICT-PD score was associated with an increased risk for incident PD in our sample and may represent a useful first screening step in future algorithms aiming to identify cases of prodromal PD. © 2020 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Perspective: Current Pitfalls in the Search for Future Treatments and Prevention of Parkinson's Disease

We are gradually becoming aware that there is more to Parkinson's disease (PD) than meets the eye. Accumulating evidence has unveiled a disease complexity that has not (yet) been incorporated into ongoing efforts aimed at slowing, halting or reversing the course of PD, likely underlying their lack of success. There is a substantial latency between the actual onset of PD pathology and our ability to confirm diagnosis, during which accumulating structural and functional damage might be too advanced for effective modification or protection. Identification at the earliest stages of the disease course in the absence of Parkinsonism is crucial if we are to intervene when it matters most. Prognostic and therapeutic inferences can only be successful if we are able to accurately predict who is at risk for developing PD and if we can differentiate amongst the considerable clinicopathologic diversity. Biomarkers can greatly improve our identification and differentiation abilities if we are able to disentangle cause and effect.

[Emerging concepts for precision medicine in Parkinson's disease with focus on genetics]

The diverse and highly individual presentations of Parkinson's disease (PD) as a complex combination of motor and non-motor symptoms are being increasingly well characterised not least through large patient cohorts applying deep phenotyping. However, in terms of treatment of PD, the approach is uniform and purely symptomatic. Better stratification strategies with better precision medicine approaches offer opportunities to improve symptomatic treatment, define first causative therapies and provide more patient-centred care. Insight from targeted therapies for monogenic forms of PD aiming at neuroprotection may pave the way for new mechanism-based interventions also for the more common idiopathic PD. Improved stratification of patients may support symptomatic treatments by predicting treatment efficacy and long-term benefit of current pharmacological or neuromodulatory therapies, e.g. in the context of emerging pharmacogenomic knowledge. Based on asymptomatic carriers with monogenic PD or patients with REM sleep behaviour disorder (RBD), first options for applying preventive treatments emerge. The implications of these treatment strategies in relation to disease progression, and the prospects of their implementation in clinical practice need to be addressed.

New hopes for disease modification in Parkinson's Disease

To date, despite numerous clinical trials, no intervention has been demonstrated to modify the progression of Parkinson's disease (PD). However, over the past decades encouraging progress has been made towards a better understanding of molecular pathways relevant for the neurodegenerative process in PD. This is also based on new insights into the genetic architecture of the disease, revealing multiple novel targets for potentially disease-modifying interventions. Important achievements have also been made in the field of risk markers and combinations thereof, in the form of risk algorithms, will hopefully soon provide the possibility to identify affected individuals at yet prediagnostic or prodromal stages of the illness. Such phases of the disease would provide an ideal window for neuroprotection trials. Taken together, these developments offer hope that a breakthrough towards modifying the course of PD might be reached. In this article we summarize various approaches currently pursued in this quest. This article is part of the special issue entitled 'The Quest for Disease-Modifying Therapies for Neurodegenerative Disorders'.

Genetics of Parkinson's disease: An introspection of its journey towards precision medicine

A substantial proportion of risk for Parkinson's disease (PD) is driven by genetics. Progress in understanding the genetic basis of PD has been significant. So far, highly-penetrant rare genetic alterations in SNCA, LRRK2, VPS35, PRKN, PINK1, DJ-1 and GBA have been linked with typical familial PD and common genetic variability at 90 loci have been linked to risk for PD. In this review, we outline the journey thus far of PD genetics, highlighting how significant advances have improved our knowledge of the genetic basis of PD risk, onset and progression. Despite remarkable progress, our field has yet to unravel how genetic risk variants disrupt biological pathways and molecular networks underlying the pathobiology of the disease. We highlight that currently identified genetic risk factors only represent a fraction of the likely genetic risk for PD. Identifying the remaining genetic risk will require us to diversify our efforts, performing genetic studies across different ancestral groups. This work will inform us on the varied genetic basis of disease across populations and also aid in fine mapping discovered loci. If we are able to take this course, we foresee that genetic discoveries in PD will directly influence our ability to predict disease and aid in defining etiological subtypes, critical steps for the implementation of precision medicine for PD.

A machine learning algorithm successfully screens for Parkinson's in web users

Objective

To develop, apply, and evaluate, a novel web‐based classifier for screening for Parkinson disease among a large cohort of search engine users.

Methods

A supervised machine learning classifier learned to distinguish web users with self‐reported Parkinson's disease from controls based on their interactions with a search engine (Bing, Microsoft). It was then applied to groups of web users with low or high risk for actual Parkinson's disease. Textual content of web queries was used to sort surfers into the different risk groups, but not for classifying users as negative or positive for Parkinson's disease. Disease detection was unsolicited. Researchers did not have access to any identifying data on users.

Results

Applying the classifier (with an estimated positive predictive value of 25%) resulted in 17,843/1,490,987 (1.2%) web users over the age of 40 years screened positive for Parkinson's disease. This percentile was higher in at‐risk groups (Fisher exact P < 0.00001), including users who searched for information regarding the disease (518/804, 64.4%), and users with non‐motor Parkinson's symptom or with an affected relative (57/1064, 5.3%). Longitudinal follow‐up revealed that in all studied groups individuals classified as having the disease showed a higher mean rate of progression in disease‐related features (t‐test P < 0.05).

Interpretation

An automatic classifier, based on mouse and keyboard interactions with a search engine, is able to reliably trace individuals at high risk for actual Parkinson's disease as well as to demonstrate more rapid progression of disease‐related signs in those who screened positive. This ability raises novel ethical issues.

Screening performance of abbreviated versions of the UPSIT smell test

BACKGROUND

Hyposmia can develop with age and in neurodegenerative conditions, including Parkinson's disease (PD). The University of Pennsylvania Smell Identification Test (UPSIT) is a 40-item smell test widely used for assessing hyposmia. However, in a number of situations, such as identifying hyposmic individuals in large populations, shorter tests are preferable.

METHODS

We assessed the ability of shorter UPSIT subsets to detect hyposmia in 891 healthy participants from the PREDICT-PD study. Shorter subsets included Versions A and B of the 4-item Pocket Smell Test (PST) and 12-item Brief Smell Identification Test (BSIT). Using a data-driven approach, we evaluated screening performances of 23,231,378 combinations of 1-7 smell items from the full UPSIT to derive "winning" subsets, and validated findings separately in another 191 healthy individuals. We then compared discriminatory UPSIT smells between PREDICT-PD participants and 40 PD patients, and assessed the performance of "winning" subsets containing discriminatory smells in PD patients.

RESULTS

PST Versions A and B achieved sensitivity/specificity of 76.8%/64.9% and 86.6%/45.9%, respectively, while BSIT Versions A and B achieved 83.1%/79.5% and 96.5%/51.8%. From the data-driven analysis, 2 "winning" 7-item subsets surpassed the screening performance of 12-item BSITs (validation sensitivity/specificity of 88.2%/85.4% and 100%/53.5%), while a "winning" 4-item subset had higher sensitivity than PST-A, -B, and even BSIT-A (validation sensitivity 91.2%). Interestingly, several discriminatory smells featured within "winning" subsets, and demonstrated high-screening performances for identifying hyposmic PD patients.

CONCLUSION

Using abbreviated smell tests could provide a cost-effective means of large-scale hyposmia screening, allowing more targeted UPSIT administration in general and PD-related settings.

Early and Accurate Identification of Parkinson Disease Among US Veterans

Early and accurate identification and management of veterans at risk for Parkinson disease is an important priority area for the US Department of Veterans Affairs because of the substantial impact on quality of life and disability-adjusted life years.

Hyposmia as a Predictive Marker of Parkinson's Disease: A Systematic Review and Meta-Analysis

Background

Hyposmia is one of the most common and best-characterized conditions that is also one of the first nonmotor features of Parkinson's disease (PD). The association of hyposmia with PD is widely accepted; however the likelihood of developing PD is unclear. Our meta-analysis aimed to investigate the risk of PD in individuals with hyposmia.

Methods

Prospective studies on humans published before December 4^th, 2018, were searched for in PubMed, Embase, Web of Science, and Cochrane Library databases. Two independent reviewers screened studies for inclusion and extracted data. We assessed the quality of studies using the Newcastle–Ottawa Scale and pooled data for analysis using random-effects models.

Results

Of the 1774 studies retrieved, seven met the inclusion criteria for this review. A total of 3272 hyposmia and 176 PD events were reported over follow-up periods ranging from 3 to 17 years. Hyposmia was associated with a 3.84-fold risk of developing PD (pooled relative risk: 3.84, 95% CI 2.12−6.95). Subgroup analyses identified few differences between different hyposmia assessment methodologies and follow-up periods.

Conclusions

Our findings suggest that deficiencies in olfaction are associated with an increased risk of developing PD. Future studies are needed to investigate whether hyposmia is a promising and feasible biomarker for the early diagnosis of PD.

More Research Is Needed on Lifestyle Behaviors That Influence Progression of Parkinson's Disease

The variability of symptoms in Parkinson's disease (PD) suggests the need for individualized treatment. A key aspect of precision medicine is lifestyle risk factor modification, known to be important in the prevention and management of chronic illness including other neurological diseases. Diet, cognitive training, exercise, and social engagement affect brain health and quality of life, but little is known of the influence of lifestyle on PD progression. Given disease heterogeneity, absence of objective outcome measures, and the confounding effects of medication, investigating lifestyle as a potential therapy in PD is challenging. This article highlights some of these challenges in the design of lifestyle studies in PD, and suggests a more coordinated international effort is required, including ongoing longitudinal observational studies. In combination with pharmaceutical treatments, healthy lifestyle behaviors may slow the progression of PD, empower patients, and reduce disease burden. For optimal care of people with PD, it is important to close this gap in current knowledge and discover whether such associations exist.

The Parkinson's phenome-traits associated with Parkinson's disease in a broadly phenotyped cohort

In order to systematically describe the Parkinson's disease phenome, we performed a series of 832 cross-sectional case-control analyses in a large database. Responses to 832 online survey-based phenotypes including diseases, medications, and environmental exposures were analyzed in 23andMe research participants. For each phenotype, survey respondents were used to construct a cohort of Parkinson's disease cases and age-matched and sex-matched controls, and an association test was performed using logistic regression. Cohorts included a median of 3899 Parkinson's disease cases and 49,808 controls, all of European ancestry. Highly correlated phenotypes were removed and the novelty of each significant association was systematically assessed (assigned to one of four categories: known, likely, unclear, or novel). Parkinson's disease diagnosis was associated with 122 phenotypes. We replicated 27 known associations and found 23 associations with a strong a priori link to a known association. We discovered 42 associations that have not previously been reported. Migraine, obsessive-compulsive disorder, and seasonal allergies were associated with Parkinson's disease and tend to occur decades before the typical age of diagnosis for Parkinson's disease. The phenotypes that currently comprise the Parkinson's disease phenome have mostly been explored in relatively small purpose-built studies. Using a single large dataset, we have successfully reproduced many of these established associations and have extended the Parkinson's disease phenome by discovering novel associations. Our work paves the way for studies of these associated phenotypes that explore shared molecular mechanisms with Parkinson's disease, infer causal relationships, and improve our ability to identify individuals at high-risk of Parkinson's disease.

Reduced habit-driven errors in Parkinson's Disease

Parkinson’s Disease can be understood as a disorder of motor habits. A prediction of this theory is that early stage Parkinson’s patients will display fewer errors caused by interference from previously over-learned behaviours. We test this prediction in the domain of skilled typing, where actions are easy to record and errors easy to identify. We describe a method for categorizing errors as simple motor errors or habit-driven errors. We test Spanish and English participants with and without Parkinson’s, and show that indeed patients make fewer habit errors than healthy controls, and, further, that classification of error type increases the accuracy of discriminating between patients and healthy controls. As well as being a validation of a theory-led prediction, these results offer promise for automated, enhanced and early diagnosis of Parkinson’s Disease.

Advances in the Research of Risk Factors and Prodromal Biomarkers of Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. With the advent of an aging population and improving life expectancy worldwide, the number of PD patients is expected to increase, which may lead to an urgent need for effective preventive and diagnostic strategies for PD. Although there is increasing research regarding the pathogenesis of PD, there is limited knowledge regarding the prevention of PD. Moreover, the diagnosis of PD depends on clinical criteria, which require the occurrence of bradykinesia and at least one symptom of rest tremor or rigidity. However, converging evidence from clinical, genetic, neuropathological, and imaging studies suggests the initiation of PD-specific pathology prior to the initial presentation of these classical motor clinical features by years or decades. This latent stage of neurodegeneration in PD is a particularly important stage for effective neuroprotective therapies, which might retard the progression or prevent the onset of PD. Therefore, the exploration of risk factors and premotor biomarkers is not only crucial to the early diagnosis of PD but is also helpful in the development of effective neuroprotection and health care strategies for appropriate populations at risk for PD. In this review, we searched and summarized ∼249 researches and 31 reviews focusing on the risk factors and prodromal biomarkers of PD and published in MEDLINE.

Predicting diagnosis of Parkinson's disease: A risk algorithm based on primary care presentations

BACKGROUND

Diagnosis of Parkinson's disease (PD) is typically preceded by nonspecific presentations in primary care.

OBJECTIVES

The objective of this study was to develop and validate a prediction model for diagnosis of PD based on presentations in primary care.

SETTING

The settings were general practices providing data for The Health Improvement Network UK primary care database.

METHODS

Data from 8,166 patients aged older than age 50 years with incident diagnosis of PD and 46,755 controls were analyzed. Likelihood ratios, sensitivity, specificity, and positive and negative predictive values for individual symptoms and combinations of presentations were calculated. An algorithm for risk of diagnosis of PD within 5 years was calculated using multivariate logistic regression analysis. Split sample analysis was used for model validation with a 70% development sample and a 30% validation sample.

RESULTS

Presentations independently and significantly associated with later diagnosis of PD in multivariate analysis were tremor, constipation, depression or anxiety, fatigue, dizziness, urinary dysfunction, balance problems, memory problems and cognitive decline, hypotension, rigidity, and hypersalivation. The discrimination and calibration of the risk algorithm were good with an area under the curve of 0.80 (95% confidence interval 0.78-0.81). At a threshold of 5%, 37% of those classified as high risk would be diagnosed with PD within 5 years and 99% of those who were not classified as high risk would not be diagnosed with PD.

CONCLUSION

This risk algorithm applied to routine primary care presentations can identify individuals at increased risk of diagnosis of PD within 5 years to allow for monitoring and earlier diagnosis of PD. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

The clinical heterogeneity of Parkinson's disease and its therapeutic implications

Although Parkinson's disease (PD) is primarily a movement disorder, there are a range of associated nonmotor symptoms, including cognitive impairment, depression and sleep disturbance. These can occur throughout the disease course, even predating the motor syndrome. However, both motor and nonmotor symptoms are variable between individual patients. Rate of disease progression is also heterogenous: although 50% have reached key milestones of either postural instability or dementia within 4 years from diagnosis, almost a quarter have a good prognosis at 10 years. In this review we discuss how a range of different factors including clinical features, pathology and genetics, have been used to describe the heterogeneity of PD. We explore the value of longitudinal studies of incident PD cohorts, based on our own experience in Cambridgeshire, to define differences in rates of disease progression and predictors of outcome, including how such studies have informed the development of prognostic models which can be used at an individual patient level. Finally, we discuss the benefits of better understanding the basis of heterogeneity of PD in terms of implications for the development and trialling of more targeted therapies for different subgroups of patients, including regenerative approaches.

The prodromes of Parkinson's disease

Whilst the diagnosis of Parkinson's disease (PD) relies on the motor triad of bradykinesia, rigidity and tremor, the underlying pathological process starts many years before these signs are overt. In this prodromal phase of PD, a diverse range of non‐motor and motor features can occur. Individually they do not allow a diagnosis of PD, but when considered together, they reflect the gradual development of the clinical syndrome. Different subgroups within the prodromal phase may exist and reflect different underlying pathology. Here, we summarise the evidence on the prodromal phase of PD in patient groups at increased risk of PD with well described prodromal features: patients with idiopathic rapid eye movement sleep behaviour disorder, patients with idiopathic anosmia and families with monogenic mutations that are closely linked to PD pathology. In addition, we discuss the information on prodromal features from ongoing studies aimed at detecting prodromal PD in the general population. It is likely that better delineation of the clinical prodromes of PD and their progression in these high‐risk groups will improve understanding of the underlying pathophysiology.

Performance of the Movement Disorders Society criteria for prodromal Parkinson's disease: A population-based 10-year study

OBJECTIVE

We aimed to identify prodromal Parkinson's disease (PD) and its predictive accuracy for incident PD in an unselected elderly population and to estimate the relevance of this approach for future neuroprotection trials.

METHODS

We applied the recently published Movement Disorders Society (MDS) research criteria for prodromal PD to participants of the prospective population-based Bruneck Study of the 2005 assessment (n = 574, ages 55-94 years). Cases of incident PD were identified at 3-year, 5-year, and 10-year follow-up visits. We calculated predictive accuracies of baseline prodromal PD status for incident cases, and, based on them, estimated sample sizes for neuroprotection trials with conversion to PD as the primary outcome.

RESULTS

Baseline status of probable prodromal PD (n = 12) had a specificity in predicting incident PD of 98.8% (95% confidence interval, 97.3%-99.5%), a sensitivity of 66.7% (29.6%-90.8%), and a positive predictive value of 40.0% (16.7%-68.8%) over 3 years. Specificity remained stable with increasing follow-up time, sensitivity decreased to 54.6% (28.0%-78.8%) over 5 years and to 35.0% (18.0%-56.8%) over 10 years, whereas positive predictive value rose to 60.0% (31.2%-83.3%) and 77.8% (44.3%-94.7%), respectively. Sample size estimates at 80% power in an intention-to-treat approach ranged from 108 to 540 patients with probable prodromal PD depending on trial duration (3-5 years) and effect size of the agent (30%-50%).

CONCLUSIONS

Our findings show that the MDS criteria for prodromal PD yield moderate to high predictive power for incident PD in a community-based setting and may thus be helpful to define target populations of future neuroprotection trials. © 2018 International Parkinson and Movement Disorder Society.