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

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.

Knowledge mapping of prodromal Parkinson's disease: A bibliometric review and analysis (2000-2023)

The prodromal period of Parkinson's disease (PD) is currently a hot topic in PD research. However, no bibliometric analysis has been conducted in this research field. This study aimed to provide a comprehensive overview of the status, hotspots, and trends in the prodromal period of PD using bibliometrics. CiteSpace and visualization of similarities viewer were used to analyze articles and reviews on the prodromal period of PD in the Web of Science Core Collection (WoSCC) database. We analyzed the data on countries, institutions, journals, authors, keywords, and cited references. In total, 909 articles from 65 countries, including the United States (n = 265, 29.15%) and Germany (n = 174, 19.14%), were included. The number of articles and reviews related to the prodromal period of PD has increased yearly. The University of Tubingen (n = 45, 4.95%), McGill University (n = 33, 3.63%), and University of London (n = 33, 3.63%) were the research institutions with the most published studies. Movement Disorders is the journal with the largest number of published papers (n = 98, 10.8%) and the most cited publications (co-citation = 7035). These publications are from 4681 authors, with Berg (n = 49, 5.39%) and Postuma (n = 40, 4.40%) publishing the most publications, and Postuma's study (n = 1206) having the most citations. Studying the nonmotor symptoms of PD precursors is a major topic in this research field. This is the first bibliometric study to comprehensively summarize the research trends and developments in the prodromal period of PD. This information identifies recent research frontiers and hotspots and provides a reference for scholars studying the prodromal period of PD.

Diet quality and prodromal Parkinson's disease probability in isolated REM sleep behavior disorder

INTRODUCTION

It is reported that a diet that lowers oxidative stress reduces the prodromal Parkinson's disease (pPD) probability as well as the risk of Parkinson's disease (PD). In this study, we evaluated whether the diet quality of patients with isolated rapid eye movement (REM) sleep behavior disorder (iRBD) were associated with the pPD probability score, PD risk markers, or prodromal markers.

METHODS

Polysomnography (PSG)-confirmed iRBD patients from the Neurology Department at Seoul National University Hospital were enrolled. We calculated the pPD probability using the "Web-based Medical Calculator for Prodromal Risk in Parkinsonism" Diet quality was assessed using the Recommended Food Score (RFS).

RESULTS

We enrolled 101 patients with iRBD. The mean RFS score of patients with iRBD was 28.23 ± 9.29, which did not differ from the general population. Among patients with iRBD, the probability of pPD did not differ between the high and low RFS groups. In patients aged <70 years, although total RFS was not correlated with pPD probability (p = 0.529, Spearman rank correlation), legume consumption was negatively correlated with pPD probability (p = 0.032): furthermore, legume consumption was significantly higher in patients with fewer prodromal markers (p = 0.016).

CONCLUSION

Diet quality assessed by RFS did not differ between the general population and patients with iRBD in Korea. Further studies are needed to confirm these protective effects of legume consumption on iRBD, which may have strong implications for the prevention and management of PD.

Increased Free Water in the Putamen in Idiopathic REM Sleep Behavior Disorder

BACKGROUND

It has been suggested that the loss of nigrostriatal dopaminergic axon terminals occurs before the loss of dopaminergic neurons in the substantia nigra (SN) in Parkinson's disease (PD). This study aimed to use free-water imaging to evaluate microstructural changes in the dorsoposterior putamen (DPP) of idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) patients, which is considered a prodromal stage of synucleinopathies.

METHODS

Free water values in the DPP, dorsoanterior putamen (DAP), and posterior SN were compared between the healthy controls (n = 48), iRBD (n = 43) and PD (n = 47) patients. In iRBD patients, the relationships between baseline and longitudinal free water values and clinical manifestations or dopamine transporter (DAT) striatal binding ratio (SBR) were analyzed.

RESULTS

Free water values were significantly higher in the DPP and posterior substantia nigra (pSN), but not in the DAP, in the iRBD and PD groups than in controls. In iRBD patients, free water values in the DPP were progressively increased and correlated with the progression of clinical manifestations and the striatal DAT SBR. Baseline free water in the DPP was negatively correlated with striatal DAT SBR and hyposmia and positively correlated with motor deficits.

CONCLUSIONS

This study demonstrates that free water values in the DPP are increased cross-sectionally and longitudinally and associated with clinical manifestations and the function of the dopaminergic system in the prodromal stage of synucleinopathies. Our findings indicate that free-water imaging of the DPP has the potential to be a valid marker of early diagnosis and progression of synucleinopathies. © 2023 International Parkinson and Movement Disorder Society.

Dessert or Poison? The Roles of Glycosylation in Alzheimer's, Parkinson's, Huntington's Disease, and Amyotrophic Lateral Sclerosis

Ministry of Education and Key Laboratory of Neurons and glial cells of the central nervous system (CNS) are modified by glycosylation and rely on glycosylation to achieve normal neural function. Neurodegenerative disease is a common disease of the elderly, affecting their healthy life span and quality of life, and no effective treatment is currently available. Recent research implies that various glycosylation traits are altered during neurodegenerative diseases, suggesting a potential implication of glycosylation in disease pathology. Herein, we summarized the current knowledge about glycosylation associated with Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and Amyotrophic lateral sclerosis (ALS) pathogenesis, focusing on their promising functional avenues. Moreover, we collected research aimed at highlighting the need for such studies to provide a wealth of disease-related glycosylation information that will help us better understand the pathophysiological mechanisms and hopefully specific glycosylation information to provide further diagnostic and therapeutic directions for neurodegenerative diseases.

Subjective Cognitive Decline in Women with Features Suggestive of Prodromal Parkinson's Disease

BACKGROUND

Cognitive deficits can be present in the prodromal phase of Parkinson's disease (PD). Subjective cognitive decline (SCD) may contribute to identifying individuals with prodromal PD.

OBJECTIVE

The objective of this study was to examine whether SCD is more likely to be present in women with features suggestive of prodromal PD compared with women without these features.

METHODS

The study population comprised 12,427 women from the Nurses' Health Study selected to investigate prodromal PD. Prodromal and risk markers of PD were assessed via self-administered questionnaires. We evaluated the association of hyposmia, constipation, and probable rapid eye movement sleep behavior disorder, three major features of prodromal PD, with SCD, adjusting for age, education, body mass index, physical activity, smoking, alcohol, caffeine intake, and depression. We also explored whether SCD was associated with the probability of prodromal PD and conducted additional analyses using data from neurocognitive tests.

RESULTS

Women experiencing the three examined nonmotor features had the worst mean SCD score and the highest odds of poor subjective cognition (odds ratio [OR] = 1.78; 95% confidence interval [CI], 1.29-2.47). This association persisted when women with objective cognitive deficits were excluded from analyses. SCD was also more common in women with a probability of prodromal PD ≥0.80, particularly among those aged younger than 75 years (OR of poor subjective cognition = 6.57 [95% CI, 2.43-17.77]). These observations were consistent with the results from analyses using neurocognitive tests, where a worse global cognitive performance was observed among women with three features.

CONCLUSIONS

Our study suggests that self-perceived cognitive decline can be present during the prodromal phase of PD. © 2023 International Parkinson and Movement Disorder Society.

Lifelong Association of Disorders Related to Military Trauma with Subsequent Parkinson's Disease

BACKGROUND

Trauma-related disorders such as traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are emerging as risk factors for Parkinson's disease (PD), but their association with development of PD and independence from comorbid disorders remains unknown.

OBJECTIVE

To examine TBI and PTSD related to early trauma in military veterans using a case-control study.

METHODS

PD was identified by International Classification of Diseases (ICD) code, recurrent PD-specific prescriptions, and availability of 5+ years of earlier records. Validation was performed by chart review by a movement disorder-trained neurologist. Control subjects were matched 4:1 by age, duration of preceding health care, race, ethnicity, birth year, and sex. TBI and PTSD were identified by ICD code and onset based on active duty. Association and interaction were measured for TBI and PTSD with PD going back 60 years. Interaction was measured for comorbid disorders.

RESULTS

A total of 71,933 cases and 287,732 controls were identified. TBI and PTSD increased odds of subsequent PD at all preceding 5-year intervals back to year -60 (odds ratio range: 1.5 [1.4, 1.7] to 2.1 [2.0, 2.1]). TBI and PTSD showed synergism (synergy index range: 1.14 [1.09, 1.29] to 1.28 [1.09, 1.51]) and additive association (odds ratio range: 2.2 [1.6, 2.8] to 2.7 [2.5, 2.8]). Chronic pain and migraine showed greatest synergy with PTSD and TBI. Effect sizes for trauma-related disorders were comparable with established prodromal disorders.

CONCLUSIONS

TBI and PTSD are associated with later PD and are synergistic with chronic pain and migraine. These findings provide evidence for TBI and PTSD as risk factors preceding PD by decades and could aid in prognostic calculation and earlier intervention. © 2023 International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Screening and Targeting Risk Factors for Prodromal Synucleinopathy: Taking Steps toward a Prescriptive Multi-modal Framework

As the prevalence of Parkinson's disease (PD) grows, so too does the population at-risk of developing PD, those in the so-called prodromal period. This period can span from those experiencing subtle motor deficits yet not meeting full diagnostic criteria or those with physiologic markers of disease alone. Several disease-modifying therapies have failed to show a neuroprotective effect. A common criticism is that neurodegeneration, even in the early motor stages, has advanced too far for neuro-restoration-based interventions to be effective. Therefore, identifying this early population is essential. Once identified, these patients could then potentially benefit from sweeping lifestyle modifications to alter their disease trajectory. Herein, we review the literature on risk factors for, and prodromal symptoms of, PD with an emphasis on ones which may be modifiable in the earliest possible stages. We propose a process for identifying this population and speculate on some strategies which may modulate disease trajectory. Ultimately, this proposal warrants prospective studies.

Early detection of Parkinson's disease through enriching the electronic health record using a biomedical knowledge graph

Introduction

Early diagnosis of Parkinson's disease (PD) is important to identify treatments to slow neurodegeneration. People who develop PD often have symptoms before the disease manifests and may be coded as diagnoses in the electronic health record (EHR).

Methods

To predict PD diagnosis, we embedded EHR data of patients onto a biomedical knowledge graph called Scalable Precision medicine Open Knowledge Engine (SPOKE) and created patient embedding vectors. We trained and validated a classifier using these vectors from 3,004 PD patients, restricting records to 1, 3, and 5 years before diagnosis, and 457,197 non-PD group.

Results

The classifier predicted PD diagnosis with moderate accuracy (AUC = 0.77 ± 0.06, 0.74 ± 0.05, 0.72 ± 0.05 at 1, 3, and 5 years) and performed better than other benchmark methods. Nodes in the SPOKE graph, among cases, revealed novel associations, while SPOKE patient vectors revealed the basis for individual risk classification.

Discussion

The proposed method was able to explain the clinical predictions using the knowledge graph, thereby making the predictions clinically interpretable. Through enriching EHR data with biomedical associations, SPOKE may be a cost-efficient and personalized way to predict PD diagnosis years before its occurrence.

α-Synuclein induces prodromal symptoms of Parkinson's disease via activating TLR2/MyD88/NF-κB pathway in Schwann cells of vagus nerve in a rat model

BACKGROUND

Increasing evidence suggests that patients with Parkinson's disease (PD) present with peripheral autonomic dysfunction (AutD) that even precedes motor deficits, through which α-synuclein can spread to the central nervous system. However, the pathological mechanisms underlying AutD in prodromal PD remain unclear. Here, we investigated the role of α-synuclein and its interplay with the activation of Schwann cells (SCs) of the vagus nerve in AutD.

METHODS

Rats were subjected to injection with adeno-associated viruses containing the human mutated A53T gene (AAV-A53T) or an empty vector into the left cervical vagus nerve and evaluated for gastrointestinal symptoms, locomotor functions, intestinal blood flow, and nerve electrophysiology. Further, we examined the impact of α-synucleinopathy on vagus nerves, SCs, and central nervous system neurons using electron microscopy, immunofluorescence, immunohistochemistry, and western blot. Finally, the role of Toll-like receptor 2 (TLR2) in regulating the neuroinflammation in the vagus nerve via MyD88 and NF-κB pathway was determined using genetic knockdown.

RESULTS

We found that rats injected with AAV-A53T in the vagus nerve exhibited prominent signs of AutD, preceding the onset of motor deficits and central dopaminergic abnormalities by at least 3 months, which could serve as a model for prodromal PD. In addition, reduced intestinal blood flow and decreased nerve conduction velocity were identified in AAV-A53T-injected rats, accompanied by disrupted myelin sheaths and swollen SCs in the vagus nerve. Furthermore, our data demonstrated that p-α-synuclein was deposited in SCs but not in axons, activating the TLR2/MyD88/NF-κB signaling pathway and leading to neuroinflammatory responses. In contrast, silencing the TLR2 gene not only reduced inflammatory cytokine expression but also ameliorated vagal demyelination and secondary axonal loss, consequently improving autonomic function in rats.

CONCLUSIONS

These observations suggest that overexpression of α-synuclein in the vagus nerve can induce symptoms of AutD in prodromal PD, and provide support for a deeper understanding of the pathological mechanisms underlying AutD and the emergence of effective therapeutic strategies for PD.

Widening the Spectrum of Risk Factors, Comorbidities, and Prodromal Features of Parkinson Disease

Importance

The prodromal phase of Parkinson disease (PD) may last for more than 10 years. Recognition of the spectrum and occurrence of risk factors, comorbidities, and prodromal features of PD can increase understanding of the causes and development of the disease and help identify individuals at risk.

Objective

To identify the association of a subsequent diagnosis of PD with a range of risk factors and prodromal features, including lifestyle factors, comorbidities, and potential extracerebral manifestations of PD.

Design, Setting, and Participants

This was a case-control study using insurance claims of outpatient consultations of patients with German statutory health insurance between January 1, 2011, and December 31, 2020. Included were patients with incident diagnosis of PD without a previous diagnosis of parkinsonism or dementia and controls matched 1:2 for age, sex, region, and earliest year of outpatient encounter.

Exposures

Exposures were selected based on previous systematic reviews, case-control and cohort studies reporting on risk factors, comorbidities, and prodromal features of PD.

Main Outcomes and Measures

Previously postulated risk factors and prodromal features of PD, using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) coding.

Results

A total of 138 345 patients with incident PD (mean [SD] age, 75.1 [9.8] years; 73 720 male [53.3%]) and 276 690 matched controls (mean [SD] age, 75.1 (9.8) years; 147 440 male [53.3%]) were identified. Study participants were followed up for a mean (SD) of 6.0 (2.0) years. Consistent with previous reports, risk factors and prodromal features associated with PD included traumatic brain injury, odds ratio (OR), 1.62; 95% CI, 1.36-1.92; alcohol misuse, OR, 1.32; 95% CI, 1.21-1.44; hypertension, OR, 1.29; 95% CI, 1.26-1.31; anosmia, OR, 2.16; 95% CI, 1.59-2.93; and parasomnias (including RBD), OR, 1.62; 95% CI, 1.42-1.84. In addition, there were associations with restless legs syndrome (OR, 4.19; 95% CI, 3.91-4.50), sleep apnea (OR, 1.45; 95% CI, 1.37-1.54), epilepsy (OR, 2.26; 95% CI, 2.07-2.46), migraine (OR, 1.21; 95% CI, 1.12-1.29), bipolar disorder (OR, 3.81; 95% CI, 3.11-4.67), and schizophrenia (OR, 4.48; 95% CI, 3.82-5.25). The following diagnoses were also found to be associated with PD: sensory impairments beyond anosmia, such as hearing loss (OR, 1.14; 95% CI, 1.09-1.20) and changes of skin sensation (OR, 1.31; 95% CI, 1.21-1.43). There were also positive associations with skin disorders (eg, seborrheic dermatitis, OR, 1.30; 95% CI, 1.15-1.46; psoriasis, OR, 1.13; 95% CI, 1.05-1.21), gastrointestinal disorders (eg, gastroesophageal reflux, OR, 1.29; 95% CI, 1.25-1.33; gastritis, OR, 1.28; 95% CI, 1.24-1.33), conditions with a potential inflammatory component (eg, seronegative osteoarthritis, OR, 1.21; 95% CI, 1.03-1.43), and diabetes types 1 (OR, 1.32; 95% CI, 1.21-1.43) and 2 (OR, 1.24; 95% CI, 1.20-1.27). Associations even 5 to 10 years before diagnosis included tremor (odds ratio [OR], 4.49; 95% CI, 3.98-5.06), restless legs syndrome (OR, 3.73; 95% CI, 3.39-4.09), bipolar disorder (OR, 3.80; 95% CI, 2.82-5.14), and schizophrenia (OR, 4.00; 95% CI, 3.31-4.85).

Conclusions and Relevance

Results of this case-control study suggest that the associations found between PD and certain risk factors, comorbidities, and prodromal symptoms in a representative population may reflect possible early extrastriatal and extracerebral pathology of PD. This may be due to shared genetic risk with PD, medication exposure, or direct causation, or represent pathophysiologically relevant factors contributing to the pathogenesis of PD.

Predictive models for the incidence of Parkinson's disease: systematic review and critical appraisal

Numerous predictive models for Parkinson's disease (PD) incidence have been published recently. However, the model performance and methodological quality of those available models are yet needed to be summarized and assessed systematically. In this systematic review, we systematically reviewed the published predictive models for PD incidence and assessed their risk of bias and applicability. Three international databases were searched. Cohort or nested case-control studies that aimed to develop or validate a predictive model for PD incidence were considered eligible. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used for risk of bias and applicability assessment. Ten studies covering 10 predictive models were included. Among them, four studies focused on model development, covering eight models, while the remaining six studies focused on model external validation, covering two models. The discrimination of the eight new development models was generally poor, with only one model reported C index > 0.70. Four out of the six external validation studies showed excellent or outstanding discrimination. All included studies had high risk of bias. Three predictive models (the International Parkinson and Movement Disorder Society [MDS] prodromal PD criteria, the model developed by Karabayir et al. and models validated by Faust et al.) are recommended for clinical application by considering model performance and resource-demanding. In conclusion, the performance and methodological quality of most of the identified predictive models for PD incidence were unsatisfactory. The MDS prodromal PD criteria, model developed by Karabayir et al. and model validated by Faust et al. may be considered for clinical use.

Evaluation of the PREDIGT score’s performance in identifying newly diagnosed Parkinson’s patients without motor examination

Several recent publications described algorithms to identify subjects with Parkinson’s disease (PD). In creating the “PREDIGT Score”, we previously developed a hypothesis-driven, simple-to-use formula to potentially calculate the incidence of PD. Here, we tested its performance in the ‘De Novo Parkinson Study’ (DeNoPa) and ‘Parkinson’s Progression Marker Initiative’ (PPMI); the latter included participants from the ‘FOllow Up persons with Neurologic Disease’ (FOUND) cohort. Baseline data from 563 newly diagnosed PD patients and 306 healthy control subjects were evaluated. Based on 13 variables, the original PREDIGT Score identified recently diagnosed PD patients in the DeNoPa, PPMI + FOUND and the pooled cohorts with area-under-the-curve (AUC) values of 0.88 (95% CI 0.83–0.92), 0.79 (95% CI 0.72–0.85), and 0.84 (95% CI 0.8–0.88), respectively. A simplified version (8 variables) generated AUC values of 0.92 (95% CI 0.89–0.95), 0.84 (95% CI 0.81–0.87), and 0.87 (0.84–0.89) in the DeNoPa, PPMI, and the pooled cohorts, respectively. In a two-step, screening-type approach, self-reported answers to a questionnaire (step 1) distinguished PD patients from controls with an AUC of 0.81 (95% CI 0.75–0.86). Adding a single, objective test (Step 2) further improved classification. Among seven biological markers explored, hyposmia was the most informative. The composite AUC value measured 0.9 (95% CI 0.88–0.91) in DeNoPa and 0.89 (95% CI 0.84–0.94) in PPMI. These results reveal a robust performance of the original PREDIGT Score to distinguish newly diagnosed PD patients from controls in two established cohorts. We also demonstrate the formula’s potential applicability to enriching for PD subjects in a population screening-type approach.

Multi-predictor modeling for predicting early Parkinson’s disease and non-motor symptoms progression

Background

Identifying individuals with high-risk Parkinson’s disease (PD) at earlier stages is an urgent priority to delay disease onset and progression. In the present study, we aimed to develop and validate clinical risk models using non-motor predictors to distinguish between early PD and healthy individuals. In addition, we constructed prognostic models for predicting the progression of non-motor symptoms [cognitive impairment, Rapid-eye-movement sleep Behavior Disorder (RBD), and depression] in de novo PD patients at 5 years of follow-up.

Methods

We retrieved the data from the Parkinson’s Progression Markers Initiative (PPMI) database. After a backward variable selection approach to identify predictors, logistic regression analyses were applied for diagnosis model construction, and cox proportional-hazards models were used to predict non-motor symptom progression. The predictive models were internally validated by correcting measures of predictive performance for “optimism” or overfitting with the bootstrap resampling approach.

Results

For constructing diagnostic models, the final model reached a high accuracy with an area under the curve (AUC) of 0.93 (95% CI: 0.91–0.96), which included eight variables (age, gender, family history, University of Pennsylvania Smell Inventory Test score, Montreal Cognitive Assessment score, RBD Screening Questionnaire score, levels of cerebrospinal fluid α-synuclein, and SNCA rs356181 polymorphism). For the construction of prognostic models, our results showed that the AUC of the three prognostic models improved slightly with increasing follow-up time. The overall AUCs fluctuated around 0.70. The model validation established good discrimination and calibration for predicting PD onset and progression of non-motor symptoms.

Conclusion

The findings of our study facilitate predicting the individual risk at an early stage based on the predictors derived from these models. These predictive models provide relatively reliable information to prevent PD onset and progression. However, future validation analysis is still needed to clarify these findings and provide more insight into the predictive models over more extended periods of disease progression in more diverse samples.

Lipid Nanoparticles: Promising Treatment Approach for Parkinson's Disease

Parkinson’s disease (PD), a neurodegenerative disorder, is a life-altering, debilitating disease exhibiting a severe physical, psychological, and financial burden on patients. Globally, approximately 7–10 million people are afflicted with this disease, with the number of cases estimated to increase to 12.9 million by 2040. PD is a progressive movement disorder with nonmotor symptoms, including insomnia, depression, anxiety, and anosmia. While current therapeutics are available to PD patients, this treatment remains palliative, necessitating alternative treatment approaches. A major hurdle in treating PD is the protective nature of the blood–brain barrier (BBB) and its ability to limit access to foreign molecules, including therapeutics. Drugs utilized presently are nonspecific and administered at dosages that result in numerous adverse side effects. Nanomedicine has emerged as a potential strategy for treating many diseases. From the array of nanomaterials available, lipid nanoparticles (LNPs) possess various advantages, including enhanced permeability to the brain via passive diffusion and specific and nonspecific transporters. Their bioavailability, nontoxic nature, ability to be conjugated to drugs, and targeting moieties catapult LNPs as a promising therapeutic nanocarriers for PD. While PD-related studies are limited, their potential as therapeutics is evident in their formulations as vaccines. This review is aimed at examining the roles and properties of LNPs that make them efficient therapeutic nanodelivery vehicles for the treatment of PD, including therapeutic advances made to date.

Who to Enroll in Parkinson Disease Prevention Trials? The Case for Composite Prodromal Cohorts

Significant progress has been made in expanding our understanding of prodromal Parkinson disease (PD), particularly for recognition of early motor and nonmotor signs and symptoms. Although identification of these prodromal features may improve our understanding of the earliest stages of PD, they are individually insufficient for early disease detection and enrollment of participants in prevention trials in most cases because of low sensitivity, specificity, and positive predictive value. Composite cohorts, composed of individuals with multiple co-occurring prodromal features, are an important resource for conducting prodromal PD research and eventual prevention trials because they are more representative of the population at risk for PD, allow investigators to evaluate the efficacy of an intervention across individuals with varying prodromal feature patterns, are able to produce larger sample sizes, and capture individuals at different stages of prodromal PD. A key challenge in identifying individuals with prodromal disease for composite cohorts and prevention trial participation is that we know little about the natural history of prodromal PD. To move toward prevention trials, it is critical that we better understand common prodromal feature patterns and be able to predict the probability of progression and phenoconversion. Ongoing research in cohort studies and administrative databases is beginning to address these questions, but further longitudinal analyses in a large population-based sample are necessary to provide a convincing and definitive strategy for identifying individuals to be enrolled in a prevention trial.

Outcome Assessment in Parkinson Disease Prevention Trials: Utility of Clinical and Digital Measures

BACKGROUND AND OBJECTIVES

The prodromal phase of Parkinson disease (PD) is accompanied by subtle clinical signs that are not sufficient for diagnosis but could potentially be measured in the context of clinical trials of therapies intended to delay or prevent more definitive clinical features. The objective of this study was to review the available literature on the presence and time course of subtle motor features in prodromal PD in the context of planning for possible clinical trials.

METHODS

We reviewed the available literature based on expert opinion. We considered a range of outcomes including measurement of clinical features, patient-reported outcomes, digital markers, and clinical diagnosis.

RESULTS

We considered these features and measures in the context of patient stratification, intermediate outcomes, and clinically relevant end points, including phenoconversion.

DISCUSSION

Substantial progress has been made in understanding how motor features evolve in the period immediately before a PD diagnosis. Digital measures hold substantial progress for measurement precision and may be additionally relevant because they can be used in naturalistic environments outside the clinic. Future studies should focus on advancing digital sensor technology and analysis and developing methods to implement available methods, particularly determination of a clinical diagnosis of PD, in a clinical trial context.

Assessment of Risk Factors and Early Presentations of Parkinson Disease in Primary Care in a Diverse UK Population

IMPORTANCE

Early features of Parkinson disease (PD) have been described through population-based studies that overrepresent White, affluent groups and may not be generalizable.

OBJECTIVE

To investigate the association between risk factors and prediagnostic presentations of PD in an ethnically diverse UK population with high socioeconomic deprivation but universal access to health care.

DESIGN, SETTING, AND PARTICIPANTS

A nested case-control study was conducted using electronic health care records on 1 016 277 individuals from primary care practices in East London to extract clinical information recorded between 1990 and February 6, 2018. The data were analyzed between September 3, 2020, and September 3, 2021. Individuals with a diagnosis of PD were compared with controls without PD or other major neurological conditions.

MAIN OUTCOMES AND MEASURES

A matched analysis (10 controls matched for each patient with PD according to age and sex) and an unmatched analysis (adjusted for age and sex) were undertaken using multivariable logistic regression to determine associations between risk factors and prediagnostic presentations to primary care with subsequent diagnosis of PD. Three time periods (<2, 2-<5, and 5-10 years before diagnosis) were analyzed separately and together.

RESULTS

Of 1 016 277 individuals included in the data set, 5699 were excluded and 1055 patients with PD and 1 009 523 controls were included in the analysis. Patients with PD were older than controls (mean [SD], 72.9 [11.3] vs 40.3 [15.2] years), and more were male (632 [59.9%] vs 516 862 [51.2%]). In the matched analysis (1055 individuals with PD and 10 550 controls), associations were found for tremor (odds ratio [OR], 145.96; 95% CI, 90.55-235.28) and memory symptoms (OR, 8.60; 95% CI, 5.91-12.49) less than 2 years before the PD diagnosis. The associations were also found up to 10 years before PD diagnosis for tremor and 5 years for memory symptoms. Among midlife risk factors, hypertension (OR, 1.36; 95% CI, 1.19-1.55) and type 2 diabetes (OR, 1.39; 95% CI, 1.19-1.62) were associated with subsequent diagnosis of PD. Associations with early nonmotor features, including hypotension (OR, 6.84; 95% CI, 3.38-13.85), constipation (OR, 3.29; 95% CI, 2.32-4.66), and depression (OR, 4.69; 95% CI, 2.88-7.63), were also noted. Associations were found for epilepsy (OR, 2.5; 95% CI, 1.63-3.83) and hearing loss (OR, 1.66; 95% CI, 1.06-2.58), which have not previously been well reported. These findings were replicated using data from the UK Biobank. No association with future PD diagnosis was found for ethnicity or deprivation index level.

CONCLUSIONS AND RELEVANCE

This study provides data suggesting that a range of comorbidities and symptoms are encountered in primary care settings before PD diagnosis in an ethnically diverse and deprived population. Novel temporal associations were observed for epilepsy and hearing loss with subsequent development of PD. The prominence of memory symptoms suggests an excess of cognitive dysfunction in early PD in this population or difficulty in correctly ascertaining symptoms in traditionally underrepresented groups.

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.

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.

Genetic factors affecting dopaminergic deterioration during the premotor stage of Parkinson disease

To estimate dopaminergic dysfunction in patients with Parkinson disease (PD) during the premotor stage and to investigate the effect of genetic factors on the trajectories. Using longitudinal dopamine transporter single-photon emission computed tomography data from 367 sporadic PD (sPD), 72 LRRK2 (G2019S), and 39 GBA (N370S) PD patients in the Parkinson's Progression Markers Initiative (PPMI) study, we estimated the temporal trajectories of putaminal-specific binding ratios using an integrating function between baseline values and their annual change rates. In order to test reproducibility, we computed another trajectory for sPD using positron emission tomography data of 38 sPD patients at Gangnam Severance Hospital (GSH). Temporal trajectories of sPD were compared between the groups separated by age at onset (AAO) and polygenic load for common PD risk variants, and also compared with genetic PD. sPD patients in both the PPMI and GSH cohorts showed similar onset of dopaminergic degeneration around 10 years before motor onset. Early-onset PD patients exhibited later onset of degeneration and a faster decline in dopaminergic activity during the premotor period than late-onset patients. sPD patients with high polygenic load were associated with earlier onset and slower progression of dopaminergic dysfunction. Compared to the sPD and LRRK2 PD groups, GBA PD patients exhibited faster deterioration of dopaminergic function during the premotor stage. Dopaminergic dysfunction in PD appears to start about 10 years before motor onset. Genetic factors may be contributing to the heterogeneity of dopaminergic deterioration during the premotor stage.

Characterizing mild cognitive impairment in prodromal Parkinson's disease: A community-based study in China

Objective

The International Parkinson and Movement Disorder Society (MDS) has published research criteria for prodromal Parkinson's disease (pPD), which includes cognitive impairment as a prodromal marker. However, the clinical features of mild cognitive impairment (MCI) in pPD remain unknown. Our study aimed to evaluate the frequency and clinical features of mild cognitive impairment of pPD in the elderly in China.

Methods

The cross‐sectional community‐based study recruited 2688 participants aged ≥50 years. Subjects were diagnosed with pPD according to the MDS criteria. Overall, 39 pPD and 22 healthy controls underwent comprehensive clinical and neuropsychological assessment. MCI was also diagnosed by the MDS criteria. Next, we investigated the relationship between clinical factors and cognition.

Results

Among the 2,663 dementia‐free and Parkinson disease (PD)‐free participants, 55 met the criteria for pPD (2.1%) and 23 pPD met the criteria for MCI. Memory, attention/working memory, and executive function were the most frequent impaired domains, and amnestic MCI multidomain phenotype was the most frequent MCI subtype (69.57%) in pPD. Additionally, correlation analysis revealed that the global cognitive performance was negatively related to UPDRS‐III score (r = −0.456, p = 0.004).

Conclusion

MCI, specifically impairment in memory, attention/working memory, and executive domain, is present at the prodromal stage of PD. In addition, cognitive performance is correlated with motor symptoms in pPD. Our results reflect that cognitive profile, combined with motor symptoms, can help clinicians to identify individuals with pPD early, as those would be the optimal candidates for neuroprotective therapy.

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.

Onset of Skin, Gut, and Genitourinary Prodromal Parkinson's Disease: A Study of 1.5 Million Veterans

BACKGROUND

Prodromal Parkinson's disease of skin, genitourinary, and gastrointestinal systems offers a unique window for understanding early disease pathogenesis and developing disease modifying treatments. However, prior studies are limited by incomplete timing information, small sample size, and lack of adjustment for known confounders. Verifying prodromal timing and identifying new disorders in these accessible organs is critically important given their broad use.

OBJECTIVE

We aimed to measure onset timing for gastrointestinal, genitourinary, and skin disorders in a large, nationwide clinically characterized cohort of 1.5 million participants.

METHODS

Patients with Parkinson's disease (n = 303,693) were identified using diagnostic codes in the medical records database of the United States Veterans Affairs healthcare system and were compared 4:1 with matched controls. Disorder prevalence and estimated onset times were assessed for 20 years preceding diagnosis.

RESULTS

The earliest significantly increased prodromal disorders were gastroesophageal reflux, sexual dysfunction, and esophageal dyskinesia at 17, 16, and 15 years before diagnosis. Estimated onset times for each disorder occurred 5.5 ± 3.4 years before the first measured increase. The earliest estimated onset times were smell/taste, upper gastrointestinal tract, and sexual dysfunction at 20.9, 20.6, and 20.1 years before diagnosis. Onset times for constipation and urinary dysfunction were notably longer by 7 and 9 years compared to prior studies in sleep disorder patients. Dermatophytosis and prostatic hypertrophy were identified as new high prevalence prodromal disorders.

CONCLUSIONS

Gastrointestinal, genitourinary, and skin disorders manifest decades before diagnosis of Parkinson's disease, reiterating their potential as sites for developing early diagnostic testing and understanding pathogenesis.

Establishment of an Ultrasound Malignancy Risk Stratification Model for Thyroid Nodules Larger Than 4 cm

Background

The incidence and mortality of thyroid cancer, including thyroid nodules > 4 cm, have been increasing in recent years. The current evaluation methods are based mostly on studies of patients with thyroid nodules < 4 cm. The aim of the current study was to establish a risk stratification model to predict risk of malignancy in thyroid nodules > 4 cm.

Methods

A total of 279 thyroid nodules > 4 cm in 267 patients were retrospectively analyzed. Nodules were randomly assigned to a training dataset (n = 140) and a validation dataset (n = 139). Multivariable logistic regression analysis was applied to establish a nomogram. The risk stratification of thyroid nodules > 4 cm was established according to the nomogram. The diagnostic performance of the model was evaluated and compared with the American College Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS), Kwak TI-RADS and 2015 ATA guidelines using the area under the receiver operating characteristic curve (AUC).

Results

The analysis included 279 nodules (267 patients, 50.6 ± 13.2 years): 229 were benign and 50 were malignant. Multivariate regression revealed microcalcification, solid mass, ill-defined border and hypoechogenicity as independent risk factors. Based on the four factors, a risk stratified clinical model was developed for evaluating nodules > 4 cm, which includes three categories: high risk (risk value = 0.8-0.9, with more than 3 factors), intermediate risk (risk value = 0.3-0.7, with 2 factors or microcalcification) and low risk (risk value = 0.1-0.2, with 1 factor except microcalcification). In the validation dataset, the malignancy rate of thyroid nodules > 4 cm that were classified as high risk was 88.9%; as intermediate risk, 35.7%; and as low risk, 6.9%. The new model showed greater AUC than ACR TI-RADS (0.897 vs. 0.855, p = 0.040), but similar sensitivity (61.9% vs. 57.1%, p = 0.480) and specificity (91.5% vs. 93.2%, p = 0.680).

Conclusion

Microcalcification, solid mass, ill-defined border and hypoechogenicity on ultrasound may be signs of malignancy in thyroid nodules > 4 cm. A risk stratification model for nodules > 4 cm may show better diagnostic performance than ACR TI-RADS, which may lead to better preoperative decision-making.

Parkinson's disease

Parkinson's disease is a recognisable clinical syndrome with a range of causes and clinical presentations. Parkinson's disease represents a fast-growing neurodegenerative condition; the rising prevalence worldwide resembles the many characteristics typically observed during a pandemic, except for an infectious cause. In most populations, 3-5% of Parkinson's disease is explained by genetic causes linked to known Parkinson's disease genes, thus representing monogenic Parkinson's disease, whereas 90 genetic risk variants collectively explain 16-36% of the heritable risk of non-monogenic Parkinson's disease. Additional causal associations include having a relative with Parkinson's disease or tremor, constipation, and being a non-smoker, each at least doubling the risk of Parkinson's disease. The diagnosis is clinically based; ancillary testing is reserved for people with an atypical presentation. Current criteria define Parkinson's disease as the presence of bradykinesia combined with either rest tremor, rigidity, or both. However, the clinical presentation is multifaceted and includes many non-motor symptoms. Prognostic counselling is guided by awareness of disease subtypes. Clinically manifest Parkinson's disease is preceded by a potentially long prodromal period. Presently, establishment of prodromal symptoms has no clinical implications other than symptom suppression, although recognition of prodromal parkinsonism will probably have consequences when disease-modifying treatments become available. Treatment goals vary from person to person, emphasising the need for personalised management. There is no reason to postpone symptomatic treatment in people developing disability due to Parkinson's disease. Levodopa is the most common medication used as first-line therapy. Optimal management should start at diagnosis and requires a multidisciplinary team approach, including a growing repertoire of non-pharmacological interventions. At present, no therapy can slow down or arrest the progression of Parkinson's disease, but informed by new insights in genetic causes and mechanisms of neuronal death, several promising strategies are being tested for disease-modifying potential. With the perspective of people with Parkinson's disease as a so-called red thread throughout this Seminar, we will show how personalised management of Parkinson's disease can be optimised.

Prodromal Parkinson disease subtypes - key to understanding heterogeneity

In Parkinson disease (PD), pathological processes and neurodegeneration begin long before the cardinal motor symptoms develop and enable clinical diagnosis. In this prodromal phase, risk and prodromal markers can be used to identify individuals who are likely to develop PD, as in the recently updated International Parkinson and Movement Disorders Society research criteria for prodromal PD. However, increasing evidence suggests that clinical and prodromal PD are heterogeneous, and can be classified into subtypes with different clinical manifestations, pathomechanisms and patterns of spatial and temporal progression in the CNS and PNS. Genetic, pathological and imaging markers, as well as motor and non-motor symptoms, might define prodromal subtypes of PD. Moreover, concomitant pathology or other factors, including amyloid-β and tau pathology, age and environmental factors, can cause variability in prodromal PD. Patients with REM sleep behaviour disorder (RBD) exhibit distinct patterns of α-synuclein pathology propagation and might indicate a body-first subtype rather than a brain-first subtype. Identification of prodromal PD subtypes and a full understanding of variability at this stage of the disease is crucial for early and accurate diagnosis and for targeting of neuroprotective interventions to ensure efficacy.

Accelerating diagnosis of Parkinson's disease through risk prediction

Background

Characterization of prediagnostic Parkinson’s Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting.

Methods

We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window.

Results

We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles.

Conclusions

Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-021-02226-4.

Cognitive Impairment in Parkinson's Disease: Epidemiology, Clinical Profile, Protective and Risk Factors

Cognitive impairment is a common non-motor symptom in Parkinson's Disease (PD) and an important source of patient disability and caregiver burden. The timing, profile and rate of cognitive decline varies widely among individuals with PD and can range from normal cognition to mild cognitive impairment (PD-MCI) and dementia (PDD). Beta-amyloid and tau brain accumulation, oxidative stress and neuroinflammation are reported risk factors for cognitive impairment. Traumatic brain injury and pesticide and tobacco exposure have also been described. Genetic risk factors including genes such as COMT, APOE, MAPT and BDNF may also play a role. Less is known about protective factors, although the Mediterranean diet and exercise may fall in this category. Nonetheless, there is conflicting evidence for most of the factors that have been studied. The use of inconsistent criteria and lack of comprehensive assessment in many studies are important methodological issues. Timing of exposure also plays a crucial role, although identification of the correct time window has been historically difficult in PD. Our understanding of the mechanism behind these factors, as well as the interactions between gene and environment as determinants of disease phenotype and the identification of modifiable risk factors will be paramount, as this will allow for potential interventions even in established PD.

The Relationship Between Anxiety Disorders and Parkinson's Disease: Clinical and Therapeutic Issues

PURPOSE OF REVIEW

This paper seeks to describe anxiety's different symptomatologic presentations in Parkinson's disease (PD), its longitudinal course and predictors, as well as its motor and non-motor correlates. It also reviews the available screening tools and different treatment modalities.

RECENT FINDINGS

In PD, longitudinal predictors of anxiety are mostly non-motor non-dopaminergic symptoms. The longitudinal course of anxiety is mainly a stable one. The Parkinson Anxiety Scale and the Geriatric Anxiety Scale are the 2 recommended screening tools. A third of PD patients suffer from an anxiety disorder at any time point. It can precede or follow PD motor symptoms. Anxiety is associated with demographic, disease-related motor and non-motor features. There is a lack of studies evaluating psychotropic treatment of anxiety in PD. Adjustment of dopaminergic treatment is indicated when anxiety is associated with motor fluctuations. DBS can be useful as well as CBT and body-mind interventions.

Is Attention-Deficit/Hyperactivity Disorder a Risk Syndrome for Parkinson's Disease?

Recent epidemiological evidence indicates that diagnosis of attention-deficit/hyperactivity disorder (ADHD) is associated with increased risk for diseases of the basal ganglia and cerebellum, including Parkinson's disease (PD). The evidence reviewed here indicates that deficits in striatal dopamine are a shared component of the causal chains that produce these disorders. Neuropsychological studies of adult ADHD, prodromal PD, and early-stage PD reveal similar deficits in executive functions, memory, attention, and inhibition that are mediated by similar neural substrates. These and other findings are consistent with the possibility that ADHD may be part of the PD prodrome. The mechanisms that may mediate the association between PD and ADHD include neurotoxic effects of stimulants, other environmental exposures, and Lewy pathology. Understanding the nature of the association between PD and ADHD may provide insight into the etiology and pathogenesis of both disorders. The possible contribution of stimulants to this association may have important clinical and public health implications.

Premotor Parkinson's disease: Overview of clinical symptoms and current diagnostic methods

Parkinson's disease (PD) is characterized by typical motor symptoms. However, recent studies show several non-motor features that may precede the development of the motor symptoms of PD. The best known premotor symptoms include hyposmia, REM sleep behavior disorder (RBD), constipation, and depression; other symptoms are excessive daytime somnolence, orthostatic hypotension and symptomatic hypotension, erectile or urinary dysfunction, musculoskeletal symptoms, pain, and global cognitive deficit. In this review, we summarize currently available diagnostic methods for these symptoms. We also briefly summarize neuroimaging, polyneuropathy, peripheral markers, and cerebrospinal fluid biomarkers that may be used in the early diagnosis of PD.

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.

Andrographolide suppresses NLRP3 inflammasome activation in microglia through induction of parkin-mediated mitophagy in in-vitro and in-vivo models of Parkinson disease

Cellular communication linking microglia activation and dopaminergic neuronal loss play an imperative role in the progression of Parkinson's disease (PD); however, underlying molecular mechanisms are not precise and require further elucidation. NLR Family Pyrin Domain Containing 3 (NLRP3) inflammasome activation is extensively studied in context to microglial activation and progressive dopaminergic neuronal loss in PD. Several pathophysiological factors such as oxidative stress, mitochondrial dysfunction impaired mitophagy plays a crucial role in activating NLRP3 inflammasome complex. Hence, regulation of microglial activation through mitophagy could be a valuable strategy in controlling microglia mediated neurodegeneration. In this study we have developed a model of inflammasome activation by combining LPS with a mitochondrial complex-I inhibitor MPP⁺. The idea of using MPP⁺ after priming mouse microglia with LPS was to disrupt mitochondria and release reactive oxygen species, which act as Signal 2 in augmenting NLRP3 assembly, thereby releasing potent inflammatory mediators such as active interleukin-1 beta (IL-1β) and IL-18. LPS-MPP⁺ combination was seen to impaired the mitophagy by inhibiting the initial step of autophagosome formation as evidenced by protein expression and confocal imaging data. Treatment with Andrographolide promoted the parkin-dependent autophagic flux formation in microglia; resulting in the removal of defective mitochondria which in turn inhibit NLRP3 inflammasome activation. Additionally, the neuroprotective role of Andrographolide in inhibiting NLRP3 activation together with salvage ATP level via promoting parkin-dependent mitophagy was seen in the substantial nigra par compacta (SNpc) region of mice brain. Furthermore, Andrographolide rescued the dopaminergic neuron loss and improved the behavioural parameters in animal model. Collectively, our results reveal the role of mitophagy in the regulation of NLRP3 inflammasome by removing defective mitochondria. In addition, andrographolide was seen to abate NLRP3 inflammasome activation in microglia and rescue dopaminergic neuron loss.

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.

Extracellular Vesicles as Nanotherapeutics for Parkinson's Disease

Extracellular vesicles (EVs) are naturally occurring membranous structures secreted by normal and diseased cells, and carrying a wide range of bioactive molecules. In the central nervous system (CNS), EVs are important in both homeostasis and pathology. Through receptor-ligand interactions, direct fusion, or endocytosis, EVs interact with their target cells. Accumulating evidence indicates that EVs play crucial roles in the pathogenesis of many neurodegenerative disorders (NDs), including Parkinson's disease (PD). PD is the second most common ND, characterized by the progressive loss of dopaminergic (DAergic) neurons within the Substantia Nigra pars compacta (SNpc). In PD, EVs are secreted by both neurons and glial cells, with either beneficial or detrimental effects, via a complex program of cell-to-cell communication. The functions of EVs in PD range from their etiopathogenetic relevance to their use as diagnostic tools and innovative carriers of therapeutics. Because they can cross the blood-brain barrier, EVs can be engineered to deliver bioactive molecules (e.g., small interfering RNAs, catalase) within the CNS. This review summarizes the latest findings regarding the role played by EVs in PD etiology, diagnosis, prognosis, and therapy, with a particular focus on their use as novel PD nanotherapeutics.

Towards early disease modification of Parkinson's disease: a review of lessons learned in the Alzheimer field

Parkinson’s disease (PD) research is beginning to focus on early disease modification and prevention. The therapeutic pipeline includes a growing range of pharmacological interventions that could theoretically intervene with the underlying disease process. It is hoped that applying such interventions in a very early stage of the disease pathology, before the onset of motor symptoms or during its early stages, may prevent or delay further disease progression. To identify people in this early disease stage, criteria for ‘prodromal PD’ have been proposed—describing people with one or more specific features that jointly constitute a variably increased risk of developing clinically manifest PD. Here, we aim to draw lessons from the field of Alzheimer’s research, which has followed a similar strategy over the last decade, including the expansion of the disease label to ‘prodromal’ stages. Importantly, none of the large and costly randomized-controlled trials aiming to slow down or prevent Alzheimer’s dementia by targeting the alleged disease pathology, i.e., amyloid-β aggregation, resulted in detectable clinical effects. Lack of sufficiently robust phase 2 trial results before moving to phase 3 studies, suboptimal participant selection, insensitive outcomes, a too narrow target focus, and trial design flaws contributed to this disappointing outcome. We discuss the various similarities between these Alzheimer’s and PD approaches, and review the design of prevention or early disease modification trials for both diseases including the potential for immunotherapy. Finally, we offer considerations to optimize the design of such trials in PD, benefiting from the lessons learned in Alzheimer’s prevention research.

The PRIAMO study: age- and sex-related relationship between prodromal constipation and disease phenotype in early Parkinson's disease

OBJECTIVES

To explore the impact of sex and age on relationship between prodromal constipation and disease phenotype in Parkinson's disease at early stages.

METHODS

A total of 385 Parkinson's disease patients from the PRIAMO study were classified according to the presence of prodromal constipation and followed for 24 months. Multivariable mixed-effect models were applied. All analyses were performed separately for sex (64.1% men) and median age (different by sex: 67 years-old in men and 68 years-old in women).

RESULTS

As for sex, prodromal constipation was associated with greater odds of attention/memory complaints and apathy symptoms in women only. As for age, prodromal constipation was associated with lower cognitive and higher apathy scores in older patients only.

CONCLUSIONS

Prodromal constipation anticipates lower cognitive performances and more severe apathy since the earliest stages in women and older patients. Sex- and age-related heterogeneity of prodromal markers of Parkinson's disease may impact disease phenotype.

Understanding motor control in health and disease: classic single (n = 1) observations

The field of neuroscience is increasingly dominated by a preferred use of big data, where analysis of large numbers has become an essential area of development. We here draw attention to the importance of smaller numbers, and more specifically, to the historical and continued importance of detailed and judiciously performed studies in single healthy volunteers or single patients with a unique clinical presentation, as an important approach to study normal functions of the nervous system, and to understand the pathophysiology underlying neurological movement disorders. We illustrate this by discussing several historical examples and by summarising Professor John Rothwell's impressive body of work in single-patient studies, highlighting some of his seminal n = 1 studies that have had a great impact on the field. In doing so, we hope to provide a powerful incentive for the next generation of neuroscientists to keep appreciating the value of detailed analyses of single observations.

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.

Clinical and Imaging Markers of Prodromal Parkinson's Disease

The diagnosis of Parkinson's disease (PD) relies on the clinical effects of dopamine deficiency, including bradykinesia, rigidity and tremor, usually manifesting asymmetrically. Misdiagnosis is common, due to overlap of symptoms with other neurodegenerative disorders such as multiple system atrophy and progressive supranuclear palsy, and only autopsy can definitively confirm the disease. Motor deficits generally appear when 50–60% of dopaminergic neurons in the substantia nigra are already lost, limiting the effectiveness of potential neuroprotective therapies. Today, we consider PD to be not just a movement disorder, but rather a complex syndrome non-motor symptoms (NMS) including disorders of sleep-wake cycle regulation, cognitive impairment, disorders of mood and affect, autonomic dysfunction, sensory symptoms and pain. Symptomatic LRRK2 mutation carriers share non-motor features with individuals with sporadic PD, including hyposmia, constipation, impaired color discrimination, depression, and sleep disturbance. Following the assumption that the pre-symptomatic gene mutation carriers will eventually exhibit clinical symptoms, their neuroimaging results can be extended to the pre-symptomatic stage of PD. The long latent phase of PD, termed prodromal-PD, represents an opportunity for early recognition of incipient PD. Early recognition could allow initiation of possible neuroprotective therapies at a stage when therapies might be most effective. The number of markers with the sufficient level of evidence to be included in the MDS research criteria for prodromal PD have increased during the last 10 years. Here, we review the approach to prodromal PD, with an emphasis on clinical and imaging markers and report results from our neuroimaging study, a retrospective evaluation of a cohort of 39 participants who underwent DAT-SPECT scan as part of their follow up. The study was carried out to see if it was possible to detect subclinical signs in the preclinical (neurodegenerative processes have commenced, but there are no evident symptoms or signs) and prodromal (symptoms and signs are present, but are yet insufficient to define disease) stages of PD.

Validation of a Parkinson Disease Predictive Model in a Population-Based Study

Parkinson disease (PD) has a relatively long prodromal period that may permit early identification to reduce diagnostic testing for other conditions when patients are simply presenting with early PD symptoms, as well as to reduce morbidity from fall-related trauma. Earlier identification also could prove critical to the development of neuroprotective therapies. We previously developed a PD predictive model using demographic and Medicare claims data in a population-based case-control study. The area under the receiver-operating characteristic curve (AUC) indicated good performance. We sought to further validate this PD predictive model. In a randomly selected, population-based cohort of 115,492 Medicare beneficiaries aged 66-90 and without PD in 2009, we applied the predictive model to claims data from the prior five years to estimate the probability of future PD diagnosis. During five years of follow-up, we used 2010-2014 Medicare data to determine PD and vital status and then Cox regression to investigate whether PD probability at baseline was associated with time to PD diagnosis. Within a nested case-control sample, we calculated the AUC, sensitivity, and specificity. A total of 2,326 beneficiaries developed PD. Probability of PD was associated with time to PD diagnosis (p < 0.001, hazard ratio = 13.5, 95% confidence interval (CI) 10.6-17.3 for the highest vs. lowest decile of probability). The AUC was 83.3% (95% CI 82.5%-84.1%). At the cut point that balanced sensitivity and specificity, sensitivity was 76.7% and specificity was 76.2%. In an independent sample of additional Medicare beneficiaries, we again applied the model and observed good performance (AUC = 82.2%, 95% CI 81.1%-83.3%). Administrative claims data can facilitate PD identification within Medicare and Medicare-aged samples.

The motor prodromes of parkinson's disease: from bedside observation to large-scale application

There is sufficient evidence that the pathological process that causes Parkinson's disease begins years before the clinical diagnosis is made. Over the last 15 years, there has been much interest in the existence of a prodrome in some patients, with a particular focus on non-motor symptoms such as reduced sense of smell, REM-sleep disorder, depression, and constipation. Given that the diagnostic criteria for Parkinson's disease depends on the presence of bradykinesia, it is somewhat surprising that there has been much less research into the possibility of subtle motor dysfunction as a pre-diagnostic pointer. This review will focus on early motor features and provide some advice on how to detect and measure them.

Autonomic dysfunction in Parkinson's disease: Implications for pathophysiology, diagnosis, and treatment

Parkinson's disease (PD) is a neurodegenerative disease with a 200 year-long research history. Our understanding about its clinical phenotype and pathogenesis remains limited, although dopaminergic replacement therapy has significantly improved patient outcomes. Autonomic dysfunction is an essential category of non-motor phenotypes that has recently become a cutting edge field that directs frontier research in PD. In this review, we initially describe the epidemiology of dysautonomic symptoms in PD. Then, we perform a meticulous analysis of the pathophysiology of autonomic dysfunction in PD and propose that the peripheral autonomic nervous system may be a key route for α-synuclein pathology propagation from the periphery to the central nervous system. In addition, we recommend that constipation, orthostatic hypotension, urinary dysfunction, erectile dysfunction, and pure autonomic failure should be viewed as prodromal dysautonomic markers in PD prediction and diagnosis. Finally, we summarize the strategies currently available for the treatment of autonomic dysfunction in PD and suggest that high-quality, better-designed, randomized clinical trials should be conducted in the future.