PREDICT-PD: identifying risk of Parkinson's disease in the community: methods and baseline results

A retrospective study of the MDS criteria for prodromal Parkinson's disease in the general population

The Movement Disorder Society developed research criteria for the detection of the prodromal phase of Parkinson's disease (PD). Accurate identification of this phase is essential for early interventions. Therefore, we investigated the diagnostic value of these research criteria in the general population. Lifelines is an ongoing cohort study of 167,000 participants from the general population of the Northern Netherlands. 160 participants self-reported to have developed PD during three rounds of follow-up of five years each. Data were available to infer six out of eleven risk markers, and six out of twelve prodromal markers. We retrospectively compared the criteria in the prodromal stage of a group of 160 'converters' with 320 age- and sex-matched controls. The overall incidence rate of PD was 0.20 per 1.000 person-years (95% CI: 0.049-0.36), increasing with age and rates were higher in men. The median probability for prodromal PD in PD-converters was 1.29% (interquartile range: 0.46-2.9), compared to 0.83% (0.39-1.8) for controls (P = 0.014). The MDS set of criteria for prodromal PD had an ROC-AUC of 0.577, and was therefore not sufficient to adequately predict conversion to PD. We were unable to predict conversion to PD in the general population using a selection of the prodromal PD research criteria. Ancillary investigations are required to improve the diagnostic accuracy of the criteria, but most are precluded from large-scale use. Strategies, including olfactory tests or alpha-synuclein seeding amplification assays may improve the detection of prodromal PD in the general population.

The Motor Dysfunction Seen in Isolated REM Sleep Behavior Disorder

BACKGROUND

Isolated Rapid Eye Movement (REM) sleep Behavior Disorder (iRBD) requires quantitative tools to detect incipient Parkinson's disease (PD).

METHODS

A motor battery was designed and compared with the Movement Disorder Society-Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS-III) in people with iRBD and controls. This included two keyboard-based tests (BRadykinesia Akinesia INcoordination tap test and Distal Finger Tapping) and two dual tasking tests (walking and finger tapping).

RESULTS

We included 33 iRBD patients and 29 controls. The iRBD group performed both keyboard-based tapping tests more slowly (P < 0.001, P = 0.020) and less rhythmically (P < 0.001, P = 0.006) than controls. Unlike controls, the iRBD group increased their walking duration (P < 0.001) and had a smaller amplitude (P = 0.001) and slower (P = 0.007) finger tapping with dual task. The combination of the most salient motor markers showed 90.3% sensitivity for 89.3% specificity (area under the ROC curve [AUC], 0.94), which was higher than the MDS-UPDRS-III (minus action tremor) (69.7% sensitivity, 72.4% specificity; AUC, 0.81) for detecting motor dysfunction.

CONCLUSION

Speed, rhythm, and dual task motor deterioration might be accurate indicators of incipient PD in iRBD. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

Designing the First Trials for Parkinson's Prevention

For decades the greatest goal of Parkinson's disease (PD) research has often been distilled to the discovery of treatments that prevent the disease or its progression. However, until recently only the latter has been realistically pursued through randomized clinical trials of candidate disease-modifying therapy (DMT) conducted on individuals after they received traditional clinical diagnosis of PD (i.e., tertiary prevention trials). Now, in light of major advances in our understanding of the prodromal stages of PD, as well as its genetics and biomarkers, the first secondary prevention trials for PD are beginning. In this review, we take stock of DMT trials to date, summarize the breakthroughs that allow the identification of cohorts at high risk of developing a traditional diagnosis of PD, and describe key design elements of secondary prevention trials and how they depend on the prodromal stage being targeted. These elements address whom to enroll, what interventions to test, and how to measure secondary prevention (i.e., slowed progression during the prodromal stages of PD). Although these design strategies, along with the biological definition, subtype classification, and staging of the disease are evolving, all are driven by continued progress in the underlying science and integrated by a broad motivated community of stakeholders. While considerable methodological challenges remain, opportunities to move clinical trials of DMT to earlier points in the disease process than ever before have begun to unfold, and the prospects for PD prevention are nowtangible.

Ethical Considerations for Identifying Individuals in the Prodromal/Early Phase of Parkinson's Disease: A Narrative Review

The ability to identify individuals in the prodromal phase of Parkinson's disease has improved in recent years, raising the question of whether and how those affected should be informed about the risk of future disease. Several studies investigated prognostic counselling for individuals with isolated REM sleep behavior disorder and have shown that most patients want to receive information about prognosis, but autonomy and individual preferences must be respected. However, there are still many unanswered questions about risk disclosure or early diagnosis of PD, including the impact on personal circumstances, cultural preferences and specific challenges associated with different profiles of prodromal symptoms, genetic testing or biomarker assessments. This narrative review aims to summarize the current literature on prognostic counselling and risk disclosure in PD, as well as highlight future perspectives that may emerge with the development of new biomarkers and their anticipated impact on the definition of PD.

The Emergence and Progression of Motor Dysfunction in Individuals at Risk of Parkinson's Disease

BACKGROUND

PREDICT-PD is a United Kingdom population-based study aiming to stratify individuals for future Parkinson's disease (PD) using a risk algorithm.

METHODS

A randomly selected, representative sample of participants in PREDICT-PD were examined using several motor assessments, including the motor section of the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS)-III, at baseline (2012) and after an average of 6 years of follow-up. We checked for new PD diagnoses in participants seen at baseline and examined the association between risk scores and incident sub-threshold parkinsonism, motor decline (increasing ≥5 points in MDS-UPDRS-III) and single motor domains in the MDS-UPDRS-III. We replicated analyses in two independent datasets (Bruneck and Parkinson's Progression Markers Initiative [PPMI]).

RESULTS

After 6 years of follow-up, the PREDICT-PD higher-risk group (n = 33) had a greater motor decline compared with the lower-risk group (n = 95) (30% vs. 12.5%, P = 0.031). Two participants (both considered higher risk at baseline) were given a diagnosis of PD during follow-up, with motor signs emerging between 2 and 5 years before diagnosis. A meta-analysis of data from PREDICT-PD, Bruneck, and PPMI showed an association between PD risk estimates and incident sub-threshold parkinsonism (odds ratio [OR], 2.01 [95% confidence interval (CI), 1.55-2.61]), as well as new onset bradykinesia (OR, 1.69 [95% CI, 1.33-2.16]) and action tremor (OR, 1.61 [95% CI, 1.30-1.98]).

CONCLUSIONS

Risk estimates using the PREDICT-PD algorithm were associated with the occurrence of sub-threshold parkinsonism, including bradykinesia and action tremor. The algorithm could also identify individuals whose motor examination experience a decline over time. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The Views of Patients with Isolated Rapid Eye Movement Sleep Behavior Disorder on Risk Disclosure

BACKGROUND

Isolated rapid eye movement sleep behavior disorder (iRBD) is associated with an increased risk of Parkinson's disease and other synucleinopathies. There is no consensus about disclosure of this risk to patients with iRBD.

OBJECTIVE

The objective of our study was to assess the experiences of risk disclosure in a group of patients with iRBD and their views on what, when, and how this should be done.

METHODS

A survey was administered to patients with iRBD to explore their experiences and views on risk disclosure.

RESULTS

Thirty-one patients with iRBD (28 males; mean age, 70 [SD 8.7] years; mean disease duration, 8.7 [SD 6.4] years) were included. A third reported they had not been informed about the link between iRBD and other conditions by clinicians at diagnosis, but 90% would have liked to have received prognostic information, and 60% indicated that this should happen at the point that iRBD was diagnosed. Most participants wanted this information to come from the clinician diagnosing and treating iRBD (90.3%). Almost three-quarters (72.2%) had searched for this information online.

CONCLUSIONS

Patients with iRBD mostly wished to have received information regarding the potential implications of iRBD when the diagnosis was made. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

Development of Parkinson Disease and Its Relationship with Incidentally Discovered White Matter Disease and Covert Brain Infarction in a Real-World Cohort

OBJECTIVE

This study aimed to examine the relationship between covert cerebrovascular disease, comprised of covert brain infarction and white matter disease, discovered incidentally in routine care, and subsequent Parkinson disease.

METHODS

Patients were ≥50 years and received neuroimaging for non-stroke indications in the Kaiser Permanente Southern California system from 2009 to 2019. Natural language processing identified incidentally discovered covert brain infarction and white matter disease and classified white matter disease severity. The Parkinson disease outcome was defined as 2 ICD diagnosis codes.

RESULTS

230,062 patients were included (median follow-up 3.72 years). A total of 1,941 Parkinson disease cases were identified (median time-to-event 2.35 years). Natural language processing identified covert cerebrovascular disease in 70,592 (30.7%) patients, 10,622 (4.6%) with covert brain infarction and 65,814 (28.6%) with white matter disease. After adjustment for known risk factors, white matter disease was associated with Parkinson disease (hazard ratio 1.67 [95%CI, 1.44, 1.93] for patients <70 years and 1.33 [1.18, 1.50] for those ≥70 years). Greater severity of white matter disease was associated with increased incidence of Parkinson disease(/1,000 person-years), from 1.52 (1.43, 1.61) in patients without white matter disease to 4.90 (3.86, 6.13) in those with severe disease. Findings were robust when more specific definitions of Parkinson disease were used. Covert brain infarction was not associated with Parkinson disease (adjusted hazard ratio = 1.05 [0.88, 1.24]).

INTERPRETATION

Incidentally discovered white matter disease was associated with subsequent Parkinson disease, an association strengthened with younger age and increased white matter disease severity. Incidentally discovered covert brain infarction did not appear to be associated with subsequent Parkinson disease. ANN NEUROL 2022;92:620-630.

Challenges of Incorporating Digital Health Technology Outcomes in a Clinical Trial: Experiences from PD STAT

Digital health technologies (DHTs) have great potential for use as clinical trial outcomes; however, practical issues need to be addressed in order to maximise their benefit. We describe our experience of incorporating two DHTs as secondary/exploratory outcome measures in PD STAT, a randomised clinical trial of simvastatin in people with Parkinson's disease. We found much higher rates of missing data in the DHTs than the traditional outcome measures, in particular due to technical and software difficulties. We discuss methods to address these obstacles in terms of protocol design, workforce training and data management.

Redefining the hypotheses driving Parkinson's diseases research

Parkinson’s disease (PD) research has largely focused on the disease as a single entity centred on the development of neuronal pathology within the central nervous system. However, there is growing recognition that PD is not a single entity but instead reflects multiple diseases, in which different combinations of environmental, genetic and potential comorbid factors interact to direct individual disease trajectories. Moreover, an increasing body of recent research implicates peripheral tissues and non-neuronal cell types in the development of PD. These observations are consistent with the hypothesis that the initial causative changes for PD development need not occur in the central nervous system. Here, we discuss how the use of neuronal pathology as a shared, qualitative phenotype minimises insights into the possibility of multiple origins and aetiologies of PD. Furthermore, we discuss how considering PD as a single entity potentially impairs our understanding of the causative molecular mechanisms, approaches for patient stratification, identification of biomarkers, and the development of therapeutic approaches to PD. The clear consequence of there being distinct diseases that collectively form PD, is that there is no single biomarker or treatment for PD development or progression. We propose that diagnosis should shift away from the clinical definitions, towards biologically defined diseases that collectively form PD, to enable informative patient stratification. N-of-one type, clinical designs offer an unbiased, and agnostic approach to re-defining PD in terms of a group of many individual diseases.

Multi-modality machine learning predicting Parkinson's disease

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.

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.

Prediagnostic Progressive Supranuclear Palsy - Insights from the UK Biobank

INTRODUCTION

Prediagnostic features of Parkinson's Disease are well described but prediagnostic Progressive Supranuclear Palsy (PSP) is less understood. The diagnosis of PSP is delayed by an average of three years after symptom onset. Understanding the changes that occur in the prediagnostic period will aid earlier diagnosis, clarify the natural history, and may aid the design of early disease-modifying therapy trials. We set out to identify motor and cognitive markers of prediagnostic PSP, with Parkinson's disease as a comparator condition, in a large prospective cohort.

METHODS

Baseline UK Biobank data from 502,504 individuals were collected between 2006 and 2010. Subsequent PSP and Parkinson's disease cases were identified from primary and secondary care electronic health records' diagnostic coding data and death registry, with 5404 matched controls.

RESULTS

176 PSP cases (time to diagnosis 7.8 ± 2.8 years) and 2526 Parkinson's disease cases (time to diagnosis 7.8 ± 2.9 years) were identified. At baseline, those later diagnosed with PSP had slower reaction times, weaker hand grip, lower fluid intelligence, prospective memory, self-rated health scores and digit recall than controls. Reaction times were correlated with time to diagnosis. The PSP group had higher mortality than both Parkinson's disease and control groups.

CONCLUSIONS

Motor slowing, cognitive dysfunction, and postural instability are clinical diagnostic features of PSP that are typically symptomatic three years before diagnosis. Objective markers of these features were evident on average 7.8 years before diagnosis. Our findings suggest the existence of a long prediagnostic phase in PSP, with subtle changes in motor and cognitive function.

Developing and assessing a new web-based tapping test for measuring distal movement in Parkinson's disease: a Distal Finger Tapping test

Disability in Parkinson's disease (PD) is measured by standardised scales including the MDS-UPDRS, which are subject to high inter and intra-rater variability and fail to capture subtle motor impairment. The BRadykinesia Akinesia INcoordination (BRAIN) test is a validated keyboard tapping test, evaluating proximal upper-limb motor impairment. Here, a new Distal Finger Tapping (DFT) test was developed to assess distal upper-limb function. Kinetic parameters of the test include kinesia score (KS20, key taps over 20 s), akinesia time (AT20, mean dwell-time on each key) and incoordination score (IS20, variance of travelling time between key taps). To develop and evaluate a new keyboard-tapping test for objective and remote distal motor function in PD patients. The DFT and BRAIN tests were assessed in 55 PD patients and 65 controls. Test scores were compared between groups and correlated with the MDS-UPDRS-III finger tapping sub-scores. Nine additional PD patients were recruited for monitoring motor fluctuations. All three parameters discriminated effectively between PD patients and controls, with KS20 performing best, yielding 79% sensitivity for 85% specificity; area under the receiver operating characteristic curve (AUC) = 0.90. A combination of DFT and BRAIN tests improved discrimination (AUC = 0.95). Among three parameters, KS20 showed a moderate correlation with the MDS-UPDRS finger-tapping sub-score (Pearson's r = - 0.40, p = 0.002). Further, the DFT test detected subtle changes in motor fluctuation states which were not reflected clearly by the MDS-UPDRS-III finger tapping sub-scores. The DFT test is an online tool for assessing distal movements in PD, with future scope for longitudinal monitoring of motor complications.

Can detection and prediction models for Alzheimer's Disease be applied to Prodromal Parkinson's Disease using explainable artificial intelligence? A brief report on Digital Neuro Signatures

Parkinson's disease (PD) is the fastest growing neurodegeneration and has a prediagnostic phase with a lot of challenges to identify clinical and laboratory biomarkers for those in the earliest stages or those 'at risk'. Despite the current research effort, further progress in this field hinges on the more effective application of digital biomarker and artificial intelligence applications at the prediagnostic stages of PD. It is of the highest importance to stratify such prediagnostic subjects that seem to have the most neuroprotective benefit from drugs. However, current initiatives to identify individuals at risk or in the earliest stages that might be candidates for future clinical trials are still challenging due to the limited accuracy and explainability of existing prediagnostic detection and progression prediction solutions. In this brief paper, we report on a novel digital neuro signature (DNS) for prodromal-PD based on selected digital biomarkers previously discovered on preclinical Alzheimer's disease. (AD). Our preliminary results demonstrated a standard DNS signature for both preclinical AD and prodromal PD, containing a ranked selection of features. This novel DNS signature was rapidly repurposed out of 793 digital biomarker features and selected the top 20 digital biomarkers that are predictive and could detect both the biological signature of preclinical AD and the biological mechanism of a-synucleinopathy in prodromal PD. The resulting model can provide physicians with a pool of patients potentially eligible for therapy and comes along with information about the importance of the digital biomarkers that are predictive, based on SHapley Additive exPlanations (SHAP). Similar initiatives could clarify the stage before and around diagnosis, enabling the field to push into unchartered territory at the earliest stages of the disease.

The remote assessment of parkinsonism supporting ongoing development of interventions in Gaucher disease

Mutations in GBA which are causative of Gaucher disease in their biallelic form, are the most common genetic risk factor for Parkinson's disease (PD). The diagnosis of PD relies upon clinically defined motor features which appear after irreversible neurodegeneration. Prodromal symptoms of PD may provide a means to predict latent pathology, years before the onset of motor features. Previous work has reported prodromal features of PD in GBA mutation carriers, however this has been insufficiently sensitive to identify those that will develop PD. The Remote Assessment of Parkinsonism Supporting Ongoing Development of Interventions in Gaucher Disease (RAPSODI GD) study assesses a large cohort of GBA mutation carriers, to aid development of procedures for earlier diagnosis of PD.

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.

Comparison between four published definitions of hyposmia in Parkinson's disease

OBJECTIVES

Hyposmia is a common feature of Parkinson's disease (PD), yet there is no standard method to define it. A comparison of four published methods was performed to explore and highlight differences.

MATERIALS AND METHODS

Olfactory testing was performed in 2097 cases of early PD in two prospective studies. Olfaction was assessed using various cut-offs, usually corrected by age and/or gender. Control data were simulated based on the age and gender structure of the PD cases and published normal ranges. Association with age, gender, and disease duration was explored by method and study cohort. Prevalence of hyposmia was compared with the age and gender-matched simulated controls. Between method agreement was measured using Cohen's kappa and Gwet's AC1.

RESULTS

Hyposmia was present in between 69.1% and 97.9% of cases in Tracking Parkinson's cases, and between 62.2% and 90.8% of cases in the Parkinson's Progression Marker Initiative, depending on the method. Between-method agreement varied (kappa 0.09-0.80, AC1 0.55-0.86). The absolute difference between PD cases and simulated controls was similar for men and women across methods. Age and male gender were positively associated with hyposmia (p < .001, all methods). Odds of having hyposmia increased with advancing age (OR:1.06, 95% CI:1.03, 1.10, p < .001). Longer disease duration had a negative impact on overall olfactory performance.

CONCLUSIONS

Different definitions of hyposmia give different results using the same dataset. A standardized definition of hyposmia in PD is required, adjusting for age and gender, to account for the background decline in olfactory performance with ageing, especially in men.

Probing the Pre-diagnostic Phase of Parkinson's Disease in Population-Based Studies

Parkinson's disease covers a wide spectrum of symptoms, ranging from early non-motor symptoms to the characteristic bradykinesia, tremor and rigidity. Although differences in the symptomatology of Parkinson's disease are increasingly recognized, there is still a lack of insight into the heterogeneity of the pre-diagnostic phase of Parkinson's disease. In this perspective, we highlight three aspects regarding the role of population-based studies in providing new insights into the heterogeneity of pre-diagnostic Parkinson's disease. First we describe several specific advantages of population-based cohort studies, including the design which overcomes some common biases, the broad data collection and the high external validity. Second, we draw a parallel with the field of Alzheimer's disease to provide future directions to uncover the heterogeneity of pre-diagnostic Parkinson's disease. Finally, we anticipate on the emergence of prevention and disease-modification trials and the potential role of population-based studies herein. In the coming years, bridging gaps between study designs will be essential to make vital advances in elucidating the heterogeneity of pre-diagnostic Parkinson's disease.

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.

Slow Motion Analysis of Repetitive Tapping (SMART) Test: Measuring Bradykinesia in Recently Diagnosed Parkinson's Disease and Idiopathic Anosmia

BACKGROUND

Bradykinesia is the defining motor feature of Parkinson's disease (PD). There are limitations to its assessment using standard clinical rating scales, especially in the early stages of PD when a floor effect may be observed.

OBJECTIVE

To develop a quantitative method to track repetitive tapping movements and to compare people in the early stages of PD, healthy controls, and individuals with idiopathic anosmia.

METHODS

This was a cross-sectional study of 99 participants (early-stage PD = 26, controls = 64, idiopathic anosmia = 9). For each participant, repetitive finger tapping was recorded over 20 seconds using a smartphone at 240 frames per second. From each video, amplitude between fingers, frequency (number of taps per second), and velocity (distance travelled per second) was extracted. Clinical assessment was based on the motor section of the MDS-UPDRS.

RESULTS

People in the early stage of PD performed the task with slower velocity (p <  0.001) and with greater frequency slope than controls (p = 0.003). The combination of reduced velocity and greater frequency slope obtained the best accuracy to separate early-stage PD from controls based on metric thresholds alone (AUC = 0.88). Individuals with anosmia exhibited slower velocity (p = 0.001) and smaller amplitude (p <  0.001) compared with controls.

CONCLUSION

We present a simple, proof-of-concept method to detect early motor dysfunction in PD. Mean tap velocity appeared to be the best parameter to differentiate patients with PD from controls. Patients with anosmia also showed detectable differences in motor performance compared with controls which may suggest that some were in the prodromal phase of PD.

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.

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.

Inflammatory Mechanisms in Parkinson's Disease: From Pathogenesis to Targeted Therapies

Inflammation is a critical factor contributing to the progressive neurodegenerative process observed in Parkinson's disease (PD). Microglia, the immune cells of the central nervous system, are activated early in PD pathogenesis and can both trigger and propagate early disease processes via innate and adaptive immune mechanisms such as upregulated immune cells and antibody-mediated inflammation. Downstream cytokines and gene regulators such as microRNA (miRNA) coordinate later disease course and mediate disease progression. Biomarkers signifying the inflammatory and neurodegenerative processes at play within the central nervous system are of increasing interest to clinical teams. To be effective, such biomarkers must achieve the highest sensitivity and specificity for predicting PD risk, confirming diagnosis, or monitoring disease severity. The aim of this review was to summarize the current preclinical and clinical evidence that suggests that inflammatory processes contribute to the initiation and progression of neurodegenerative processes in PD. In this article, we further summarize the data about main inflammatory biomarkers described in PD to date and their potential for regulation as a novel target for disease-modifying pharmacological strategies.

Late onset depression: dopaminergic deficit and clinical features of prodromal Parkinson's disease: a cross-sectional study

BACKGROUND

Late onset depression (LOD) may precede the diagnosis of Parkinson's disease (PD) or dementia with Lewy bodies (DLB). We aimed to determine the rate of clinical and imaging features associated with prodromal PD/DLB in patients with LOD.

METHODS

In a cross-sectional design, 36 patients with first onset of a depressive disorder (Diagnostic and Statistical Manual of Mental Disorders IV criteria) diagnosed after the age of 55 (LOD group) and 30 healthy controls (HC) underwent a detailed clinical assessment. In addition, 28/36 patients with LOD and 20/30 HC underwent a head MRI and 29/36 and 25/30, respectively, had dopamine transporter imaging by ¹²³I-ioflupane single-photon emission computed tomography (SPECT) imaging. Image analysis of both scans was performed by a rater blind to the participant group. Results of clinical assessments and imaging results were compared between the two groups.

RESULTS

Patients with LOD (n=36) had significantly worse scores than HC (n=30) on the PD screening questionnaire (mean (SD) 1.8 (1.9) vs 0.8 (1.2); p=0.01), Movement Disorder Society Unified Parkinson's Disease Rating Scale total (mean (SD) 19.2 (12.7) vs 6.1 (5.7); p<0.001), REM-sleep behaviour disorder screening questionnaire (mean (SD) 4.3 (3.2) vs 2.1 (2.1); p=0.001), Lille Apathy Rating Scale (mean (SD) -23.3 (9.6) vs -27.0 (4.7); p=0.04) and the Scales for Outcomes in PD-Autonomic (mean (SD) 14.9 (8.7) vs 7.7 (4.9); p<0.001). Twenty-four per cent of patients with LOD versus 4% HC had an abnormal ¹²³I-ioflupane SPECT scan (p=0.04).

CONCLUSIONS

LOD is associated with increased rates of motor and non-motor features of PD/DLB and of abnormal ¹²³I-ioflupane SPECTs. These results suggest that patients with LOD should be considered at increased risk of PD/DLB.

Olfactory Dysfunction in a Mexican Population Outside of COVID-19 Pandemic: Prevalence and Associated Factors (the OLFAMEX Study)

PURPOSE OF REVIEW

To study the prevalence of olfactory loss and its associated factors in a Mexican population a cross-sectional analytical study based on a population interviewed about health, epidemiologic aspects, and sense of smell (tested with four scents: rose, banana, perfume, and gas) was conducted to evaluate olfactory detection, memory, and identification. Levels of sense of smell perception were determined when the participants detected, recognized, or identified all (normosmia), 1-3 (hyposmia), or none (anosmia) of the odorants. Associated factors of olfactory dysfunction were identified by multivariate analysis (odds ratio, 95%CI).

RECENT FINDINGS

Olfactory dysfunction is a prevalent disorder affecting up to 20% of the general population. In addition to viral infection, including COVID-19, a number of other causes and factors may also be involved. 1,956 surveys were conducted and 1,921 were analyzed. Most of the participants (62.1%) were women. The general prevalence of olfactory dysfunction, regarding detection, was 7.2% (7.1% hyposmia, 0.1% anosmia). Age-related olfactory deterioration was observed in both sexes from the 5th decade of life (OR 2.74, p = 0.0050). Women showed better olfactory identification (OR 0.73, p = 0.0010). Obesity (OR 1.97, p = 0.0070), low educational level, bad/very bad self-perceived olfactory function (OR 2.74, p = 0.0050), olfactory loss for less than one week (OR 1.35, p = 0.0030), exposure to toxics/irritants (OR 1.31, p = 0.0030), active smoking (OR 1.58, p < 0.0010), and type 2 diabetes mellitus (OR 2.68, 95%CI 1.74-4.10, p < 0.0001) were identified as factors associated with olfactory dysfunction. These results in a Mexican population suggest better olfactory identification (verbalization) in females. Age was a determining factor in the olfactory deterioration process and obesity and diabetes mellitus were also associated with olfactory disorders. Finally, these findings reinforce the differential diagnosis with other potential causes of sense of smell loss, during the COVID-19 outbreak.

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.

Glucocerebrosidase Defects as a Major Risk Factor for Parkinson's Disease

Heterozygous mutations of the GBA1 gene, encoding for lysosomal enzyme glucocerebrosidase (GCase), occur in a considerable percentage of all patients with sporadic Parkinson's disease (PD), varying between 8% and 12% across the world. Genome wide association studies have confirmed the strong correlation between PD and GBA1 mutations, pointing to this element as a major risk factor for PD, possibly the most important one after age. The pathobiological mechanisms underlying the link between a defective function of GCase and the development of PD are still unknown and are currently the focus of intense investigation in the community of pre-clinical and clinical researchers in the PD field. A major controversy regards the fact that, despite the unequivocal correlation between the presence of GBA1 mutations and the risk of developing PD, only a minority of asymptomatic carriers with GBA1 mutations convert to PD in their lifetime. GBA1 mutations reduce the enzymatic function of GCase, impairing lysosomal efficiency and the cellular ability to dispose of pathological alpha-synuclein. Changes in the cellular lipidic content resulting from the accumulation of glycosphingolipids, triggered by lysosomal dysfunction, may contribute to the pathological modification of alpha-synuclein, due to its ability to interact with cell membrane lipids. Mutant GCase can impair mitochondrial function and cause endoplasmic reticulum stress, thereby impacting on cellular energy production and proteostasis. Importantly, reduced GCase activity is associated with clear activation of microglia, a major mediator of neuroinflammatory response within the brain parenchyma, which points to neuroinflammation as a major consequence of GCase dysfunction. In this present review article, we summarize the current knowledge on the role of GBA1 mutations in PD development and their phenotypic correlations. We also discuss the potential role of the GCase pathway in the search for PD biomarkers that may enable the development of disease modifying therapies. Answering these questions will aid clinicians in offering more appropriate counseling to the patients and their caregivers and provide future directions for PD preclinical research.

Is Low Psychomotor Speed a Marker of Brain Vulnerability in Late Life? Digit Symbol Substitution Test in the Prediction of Alzheimer, Parkinson, Stroke, Disability, and Depression

BACKGROUND

Dementia, stroke, depression, and disability are frequent in late life and are major causes of quality of life disruption and family burden. Even though each of these disorders relies on specific pathogenic processes, a common clinical manifestation is psychomotor slowing.

OBJECTIVE

We assessed the relevance of a simple marker of low psychomotor speed in predicting several brain outcomes: dementia, Alzheimer's disease (AD), Parkinson's disease (PD), stroke, depressive symptoms, and disability in activities of daily living (ADL) and instrumental ADL (IADL).

METHODS

PAQUID is a population-based study involving 3,777 individuals aged 65 or older prospectively followed-up with repeated clinical evaluations. After 10 years, 437 participants developed dementia, 333 developed AD, 71 developed PD, 207 reported incident stroke, 404 developed disability in ADL, 994 in IADL, and 494 developed depressive symptomology. Psychomotor speed was measured with the digit symbol substitution test (DSST). Cox proportional hazards models controlled for several confounders assessed the risk of incident outcomes.

RESULTS

Participants with low DSST performance had increased risk of incident all-type dementia (hazard ratio [HR] 3.41, p < 0.0001) and AD-type dementia (HR 3.18, p < 0.0001). Higher risk for PD (HR 2.98, p = 0.04), IADL (HR 1.82, p < 0.0001), ADL disability (HR 1.95, p = 0.001), depressive symptoms (HR 1.53, p = 0.03), and a statistical trend for stroke (HR 1.88, p = 0.09) was also found.

CONCLUSION

Low psychomotor speed is associated with an increased risk of developing various brain outcomes: dementia, AD, PD, disability, depressive symptoms, and marginally stroke. Low psychomotor speed may be the consequence of a number of discrete cerebral abnormalities and could be considered as a marker of brain vulnerability. In clinical practice, a low score in DSST should be seen as a warning sign of possible negative evolution.

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.

The BRadykinesia Akinesia INcoordination (BRAIN) Tap Test: Capturing the Sequence Effect

BACKGROUND

The BRadykinesia Akinesia INcoordination (BRAIN) tap test is an online keyboard tapping task that has been previously validated to assess upper limb motor function in Parkinson's disease (PD).

OBJECTIVES

To develop a new parameter that detects a sequence effect and to reliably distinguish between PD patients on and off medication. In addition, we sought to validate a mobile version of the test for use on smartphones and tablet devices.

METHODS

The BRAIN test scores in 61 patients with PD and 93 healthy controls were compared. A range of established parameters captured number and accuracy of alternate taps. The new velocity score recorded the intertap speed. Decrement in the velocity score was used as a marker for the sequence effect. In the validation phase, 19 PD patients and 19 controls were tested using different hardware including mobile devices.

RESULTS

Quantified slopes from the velocity score demonstrated bradykinesia (sequence effect) in PD patients (slope cut-off -0.002) with 58% sensitivity and 81% specificity (discovery phase of the study) and 65% sensitivity and 88% specificity (validation phase). All BRAIN test parameters differentiated between on and off medication states in PD. Differentiation between PD patients and controls was possible on all hardware versions of the test.

CONCLUSION

The BRAIN tap test is a simple, user-friendly, and free-to-use tool for the assessment of upper limb motor dysfunction in PD, which now includes a measure of bradykinesia.

Parkinsonism Risk Factors in Salt Lake City, Utah: A Community-Based Study

BACKGROUND

The prevalence of dream enactment behavior and other risk factors for a parkinsonian disorder is not well documented.

METHODS

A survey on prevalence of parkinsonism risk factors was designed using two validated instruments (REM behavior disorder single item question, bowel movement frequency for constipation) and three exploratory instruments (for hallucinations, cognitive and olfactory complaints.) It was sent by mail and email to patients aged 50 and over at two University of Utah community clinics in Salt Lake City. A total of 7888 unique patients were sent the survey, and 1607 responses were recorded (response rate 20%). Those whose age was missing (n = 117) or less than 50 years (n = 10) were excluded from the analysis.

RESULTS

Of the 1406 without personal diagnosis of neurodegenerative disease 62.7% were female, and median age was 63. Family history (FH) of Parkinson's disease was endorsed by 9%, constipation (defined as a bowel movement less than once per day) by 19%, mild cognitive complaints (MCI) 15.8%, dream enactment 13.7%, subjective hyposmia or anosmia 18.2%, and at least one potential psychotic symptom in 37.6%. Multivariable logistic regression showed male gender, mild cognitive complaints, hearing voices, and at least one potentially psychotic symptom to be significantly associated with dream enactment.

CONCLUSIONS

This survey shows that dream enactment, a strong predictor of risk for synucleinopathy, is relatively common in the older population; because such individuals rarely come to medical attention of a sleep clinic, such survey research may be useful to identify and recruit at-risk individuals for trials aimed at preventing neurodegenerative 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.

State-level prevalence, health service use, and spending vary widely among Medicare beneficiaries with Parkinson disease

State-level variations in disease, healthcare utilization, and spending influence healthcare planning at federal and state levels and should be examined to understand national disparities in health outcomes. This descriptive study examined state-level variations in Parkinson disease (PD) prevalence, patient characteristics, Medicare spending, out-of-pocket costs, and health service utilization using data on 27.5 million Medicare beneficiaries in the US in 2014. We found that 45.8% (n = 179,496) of Medicare beneficiaries diagnosed with PD were women; 26.1% (n = 102,205) were aged 85+. The District of Columbia, New York, Illinois, Connecticut, and Florida had the highest age-, race-, and sex-adjusted prevalence of Parkinson disease among Medicare beneficiaries in the US. Women comprised over 48.5% of PD patient populations in West Virginia, Kentucky, Mississippi, Louisiana, and Arkansas. More than 31% of the PD populations in Connecticut, Pennsylvania, Hawaii, and Rhode Island were aged 85+. PD patients who were "dual-eligible"-receiving both Medicare and Medicaid benefits-also varied by state, from <10% to >25%. Hospitalizations varied from 304 to 653 stays per 1000 PD patients and accounted for 26.5% of the 7.9 billion United States Dollars (USD) paid by the Medicare program for healthcare services delivered to our sample. A diagnosis of PD was associated with greater healthcare use and spending. This study provides initial evidence of substantial geographic variation in PD patient characteristics, health service use, and spending. Further study is necessary to inform the development of state- and federal-level health policies that are cost-efficient and support desired outcomes for PD patients.

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.

Advances in markers of prodromal Parkinson disease

Efforts to develop neuroprotective therapy for Parkinson disease (PD) are focusing on the early stages of disease, which offer the best opportunity to intervene. Early PD can be divided into preclinical, prodromal and clinical stages; in this Review, we focus on the prodromal stage and markers that can be used to identify prodromal PD. We consider the necessary properties of a marker, before providing an overview of the proven and potential markers of prodromal PD, including clinical nonmotor markers, clinical motor markers, neuroimaging markers and tissue biomarkers. Markers for which the ability to predict conversion to PD is supported by the strongest evidence include olfactory loss, REM sleep behaviour disorder and constipation. Markers with the highest diagnostic strength include REM sleep behaviour disorder, dopaminergic imaging and subtle motor parkinsonism. The lead time - the period between the appearance of a marker and conversion to PD - is highly variable between markers, ranging from 5 years for impaired motor performance to >20 years for autonomic symptoms. The cost of screening for these markers also varies dramatically: some require just questionnaires, whereas others require sophisticated scanning techniques. Finally, we summarize how prodromal and risk markers can be combined to estimate the probability that an individual has prodromal PD, with a focus on the International Parkinson Disease and Movement Disorders Society (MDS) Prodromal Parkinson Criteria.

An early diagnosis is not the same as a timely diagnosis of Parkinson's disease

Parkinson’s disease is a common neurodegenerative condition that has significant costs to the individual patient and to society. The pathology starts up to a decade before symptoms are severe enough to allow a diagnosis using current criteria. Although the search for disease-modifying treatment continues, it is vital to understand what the right time is for diagnosis. Diagnosis of Parkinson’s disease is based on the classic clinical criteria, but the presence of other clinical features and disease biomarkers may allow earlier diagnosis, at least in a research setting. In this review, we identify the benefits of an early diagnosis, including before the classic clinical features occur. However, picking the right point for a “timely” diagnosis will vary depending on the preferences of the individual patient, efficacy (or existence) of disease-modifying treatment, and the ability for health systems to provide support and management for individuals at every stage of the disease. Good evidence for the quality-of-life benefits of existing symptomatic treatment supports the argument for earlier diagnosis at a time when symptoms are already present. This argument would be significantly bolstered by the development of disease-modifying treatments. Benefits of early diagnosis and treatment would affect not only the individual (and their families) but also the wider society and the research community. Ultimately, however, shared decision-making and the principles of autonomy, beneficence, and non-maleficence will need to be applied on an individual basis when considering a “timely” diagnosis.

Gut Microbiota Dysfunction as Reliable Non-invasive Early Diagnostic Biomarkers in the Pathophysiology of Parkinson's Disease: A Critical Review

Recent investigations suggest that gut microbiota affects the brain activity through the microbiota-gut-brain axis under both physiological and pathological disease conditions like Parkinson's disease. Further dopamine synthesis in the brain is induced by dopamine producing enzymes that are controlled by gut microbiota via the microbiota-gut-brain axis. Also alpha synuclein deposition and the associated neurodegeneration in the enteric nervous system that increase intestinal permeability, oxidative stress, and local inflammation, accounts for constipation in Parkinson's disease patients. The trigger that causes blood brain barrier leakage, immune cell activation and inflammation, and ultimately neuroinflammation in the central nervous system is believed to be due to the chronic low-grade inflammation in the gut. The non-motor symptoms that appear years before motor symptoms could be reliable early biomarkers, if they could be correlated with the established and reliable neuroimaging techniques or behavioral indices. The future directions should therefore, focus on the exploration of newer investigational techniques to identify these reliable early biomarkers and define the specific gut microbes that contribute to the development of Parkinson's disease. This ultimately should pave the way to safer and novel therapeutic approaches that avoid the complications of the drugs delivered today to the brain of Parkinson's disease patients.

The BRAIN test: a keyboard-tapping test to assess disability and clinical features of multiple sclerosis

Background

The BRadykinesia Akinesia INcordination (BRAIN) test is an online keyboard-tapping test previously validated as a sensitive tool for detecting signs of Parkinson’s disease.

Objectives

To determine whether the BRAIN test can measure disability in MS and identify the presence of pyramidal or cerebellar dysfunction.

Methods

Kinesia scores (KS, number of key taps in 30 s), akinesia times (AT, mean dwell time on each key) and incoordination scores (IS, variance of travelling time between keys) were calculated in 39 MS patients. These were correlated against the Expanded Disability Status Scale (EDSS) scores, pyramidal and cerebellar functional system scores and 9-hole peg test scores.

Results

EDSS correlated with KS (r = − 0.594, p < 0.001), AT (r = 0.464, p = 0.003) and IS (r = 0.423, p = 0.007). 9-HPT scores strongly correlated with KS (r = 0.926, p < 0.001). Pyramidal scores correlated with KS (r = − 0.517, p < 0.001). Cerebellar scores correlated with KS (r = − 0.665, p < 0.001), AT (r = 0.567, p < 0.001) and IS (r = 0.546, p = 0.007). Receiver operating characteristic curves demonstrate that KS can distinguish between the presence or absence of pyramidal and cerebellar dysfunction with area under curve 0.840 (p < 0.001) and 0.829 (p < 0.001), respectively.

Conclusions

The BRAIN test can remotely measure disability in MS. Specific scores differ according to the presence and severity of pyramidal or extrapyramidal dysfunction. It demonstrates huge potential in monitoring disease progression in clinical trials.

Electronic supplementary material

The online version of this article (10.1007/s00415-017-8690-x) contains supplementary material, which is available to authorized users.

Variation in Recent Onset Parkinson's Disease: Implications for Prodromal Detection

BACKGROUND

The detection of prodromal Parkinson's disease (PD) is desirable to test drugs with neuroprotective potential, but will be affected by known disease variations.

OBJECTIVE

To assess the prevalence of four key non-motor prodromal PD markers, and evaluate the sensitivity of case detection when non-motor screening tools for prodromal PD are implemented in an early clinical PD cohort.

METHODS

Hyposmia (University of Pennsylvania smell identification test ≤15th centile or Sniffin' Sticks at or ≤10th centile corrected for age and sex), rapid-eye movement sleep behaviour disorder (RBD questionnaire >4), constipation (<1 daily spontaneous bowel motion) and depression (Leeds >6) were recorded in recent onset PD cases, and proposed non-motor screening criteria applied.

RESULTS

In 1,719 PD cases, mean age 68.6 years (SD 8.1), 65.5% male, mean disease duration 1.3 years (SD 0.9), 72.2% were hyposmic, 43.3% had RBD, 22.1% depression, and 21.5% constipation. 11.6% of cases had no key non-motor features, 38.8% one, 32.1% two, 15.5% three, and 2.0% all four. Increasing numbers of non-motor features were associated with younger age (p = 0.019), higher motor scores (p < 0.001), more postural instability gait difficulty (PIGD) (p < 0.001), greater cognitive impairment (p < 0.001) and higher total non-motor burden (p < 0.001). Cases with hyposmia alone were younger (p < 0.001), had less severe cognitive (p = 0.006) and other non-motor features (p < 0.001). All screening criteria selected younger patients (p = 0.001, p < 0.001), three of four greater overall non-motor burden (p = 0.005, p < 0.001), and inclusion of RBD more cognitive impairment (p = 0.003, p = 0.001) and PIGD (p = 0.004, p = 0.001).

CONCLUSIONS

Varying sensitivity levels, and age and phenotype selectivity, are found when different non-motor screening methods to detect prodromal PD are applied to an early clinical PD cohort.

Can Biomarkers Help the Early Diagnosis of Parkinson's Disease?

Parkinson's disease (PD) is a complex neurodegenerative disease with progressive loss of dopamine neurons. PD patients usually manifest a series of motor and non-motor symptoms. In order to provide better early diagnosis and subsequent disease-modifying therapies for PD patients, there is an urgent need to identify sensitive and specific biomarkers. Biomarkers can be divided into four categories: clinical, imaging, biochemical, and genetic. Ideal biomarkers not only improve our understanding of PD pathogenesis and progression, but also provide benefits for early risk evaluation and clinical diagnosis of PD. Although many efforts have been made and several biomarkers have been extensively investigated, few if any have been found useful for early diagnosis. Here, we summarize recent developments in the discovered biomarkers of PD and discuss their merits and limitations for the early diagnosis of PD.