The heterogeneity of early Parkinson's disease: a cluster analysis on newly diagnosed untreated patients

Lower Energy Intake among Advanced vs. Early Parkinson's Disease Patients and Healthy Controls in a Clinical Lunch Setting: A Cross-Sectional Study

Unintentional weight loss has been observed among Parkinson's disease (PD) patients. Changes in energy intake (EI) and eating behavior, potentially caused by fine motor dysfunction and eating-related symptoms, might contribute to this. The primary aim of this study was to investigate differences in objectively measured EI between groups of healthy controls (HC), early (ESPD) and advanced stage PD patients (ASPD) during a standardized lunch in a clinical setting. The secondary aim was to identify clinical features and eating behavior abnormalities that explain EI differences. All participants (n = 23 HC, n = 20 ESPD, and n = 21 ASPD) went through clinical evaluations and were eating a standardized meal (200 g sausages, 400 g potato salad, 200 g apple purée and 500 mL water) in front of two video cameras. Participants ate freely, and the food was weighed pre- and post-meal to calculate EI (kcal). Multiple linear regression was used to explain group differences in EI. ASPD had a significantly lower EI vs. HC (-162 kcal, p < 0.05) and vs. ESPD (-203 kcal, p < 0.01) when controlling for sex. The number of spoonfuls, eating problems, dysphagia and upper extremity tremor could explain most (86%) of the lower EI vs. HC, while the first three could explain ~50% vs. ESPD. Food component intake analysis revealed significantly lower potato salad and sausage intakes among ASPD vs. both HC and ESPD, while water intake was lower vs. HC. EI is an important clinical target for PD patients with an increased risk of weight loss. Our results suggest that interventions targeting upper extremity tremor, spoonfuls, dysphagia and eating problems might be clinically useful in the prevention of unintentional weight loss in PD. Since EI was lower in ASPD, EI might be a useful marker of disease progression in PD.

Cognition, function and awareness of disease impact in early Parkinson's and Huntington's disease

Purpose: Patients with Parkinson's and Huntington's Disease (PD and HD) present impairments in cognitively challenging everyday activities. This study contrasts these two basal ganglia disorders on the ability to perform daily life- like tasks and their level of awareness regarding the disease impact on function.Methods: 19 controls, 10 early-onset PD, 20 early stage PD, and 15 early manifest HD patients were compared in the "EcoKitchen," a virtual reality task with increasing executive load, the "Behavioural Assessment of Dysexecutive Syndrome battery - BADS," and "The Adults and Older Adults Functional Assessment Inventory - IAFAI," a self-report functional questionnaire. The EcoKitchen clinical correlates were investigated.Results: All clinical groups presented slower EcoKitchen performance than controls, however, only HD patients showed decreased accuracy. HD and PD patients exhibited reduced BADS scores compared to the other study participants. Importantly, on the IAFAI, PD patients signalled more physically related incapacities and HD patients indicated more cognitively related incapacities. Accordingly, the EcoKitchen performance was significantly associated with PD motor symptom severity.Conclusions: Our findings suggest differential disease impact on cognition and function across PD and HD patients, with preserved awareness regarding disease- related functional sequelae. These observations have important implications for clinical management, research and rehabilitation.Implications for rehabilitationPatients with early stage Parkinson's and Huntington's disease have diagnosis-specific impairments in the performance of executively demanding everyday activities and, yet, show preserved awareness about the disease impact on their daily life.An active involvement of patients in the rehabilitation process should be encouraged, as their appraisal of the disease effects can help on practical decisions about meaningful targets for intervention, vocational choices, quality-of-life issues and/or specific everyday skills to boost.The EcoKitchen, a non-immersive virtual reality task, can detect and quantify early deficits in everyday-like tasks and is therefore a valuable tool for assessing the effects of rehabilitation strategies on the functional cognition of these patients.Rehabilitation efforts in the mild stages of Parkinson's and Huntington's disease should be aware of greater time needs from the patients in the performance of daily life tasks, target executive skills, and give a more prominent role to patients in symptoms report and management.

Clinically relevant connectivity features define three subtypes of Parkinson's disease patients

Parkinson's disease (PD) is characterized by complex clinical symptoms, including classic motor and nonmotor disturbances. Patients with PD vary in clinical manifestations and prognosis, which point to the existence of subtypes. This study aimed to find the fiber connectivity correlations with several crucial clinical symptoms and identify PD subtypes using unsupervised clustering analysis. One hundred and thirty-four PD patients and 77 normal controls were enrolled. Canonical correlation analysis (CCA) was performed to define the clinically relevant connectivity features, which were then used in the hierarchical clustering analysis to identify the distinct subtypes of PD patients. Multimodal neuroimaging analyses were further used to explore the neurophysiological basis of these subtypes. The methodology was validated in an independent data set. CCA revealed two significant clinically relevant patterns (motor-related pattern and depression-related pattern; r = .94, p < .001 and r = .926, p = .001, respectively) among PD patients, and hierarchical clustering analysis identified three neurophysiological subtypes ("mild" subtype, "severe depression-dominant" subtype and "severe motor-dominant" subtype). Multimodal neuroimaging analyses suggested that the patients in the "severe depression-dominant" subtype exhibited widespread disruptions both in function and structure, while the other two subtypes exhibited relatively mild abnormalities in brain function. In the independent validation, three similar subtypes were identified. In conclusion, we revealed heterogeneous subtypes of PD patients according to their distinct clinically relevant connectivity features. Importantly, depression symptoms have a considerable impact on brain damage in patients with PD.

Facial expressions recognition and discrimination in Parkinson's disease

Emotion processing impairment is a common non-motor symptom in Parkinson's Disease (PD). Previous literature reported conflicting results concerning, in particular, the performance for different emotions, the relation with cognitive and neuropsychiatric symptoms and the affected stage of processing. This study aims at assessing emotion recognition and discrimination in PD. Recognition of six facial expressions was studied in order to clarify its relationship with motor, cognitive and neuropsychiatric symptoms. Sensitivity in discriminating happy and fearful faces was investigated to address controversial findings on impairment in early stages of emotion processing. To do so, seventy PD patients were tested with the Ekman 60 Faces test and compared with 46 neurologically unimpaired participants. Patients' performances were correlated with clinical scales and neuropsychological tests. A subsample of 25 PD patients and 25 control participants were also tested with a backward masking paradigm for sensitivity in happiness and fear discrimination. Results showed that PD patients were impaired in facial emotion recognition, especially for fearful expressions. The performance correlated with perceptual, executive and general cognitive abilities, but facial expression recognition deficits were present even in cognitively unimpaired patients. In contrast, patients' sensitivity in backward masking tasks was not reduced as compared to controls. Taken together our data demonstrate that facial emotion recognition, and fear expression in particular, is critically affected by neurodegeneration in PD and related to cognitive abilities; however, it appears before other cognitive impairments. Preserved performances in discriminating shortly presented facial expressions, suggest unimpaired early stages of emotion processing.

Data science in neurodegenerative disease: its capabilities, limitations, and perspectives

PURPOSE OF REVIEW

With the advancement of computational approaches and abundance of biomedical data, a broad range of neurodegenerative disease models have been developed. In this review, we argue that computational models can be both relevant and useful in neurodegenerative disease research and although the current established models have limitations in clinical practice, artificial intelligence has the potential to overcome deficiencies encountered by these models, which in turn can improve our understanding of disease.

RECENT FINDINGS

In recent years, diverse computational approaches have been used to shed light on different aspects of neurodegenerative disease models. For example, linear and nonlinear mixed models, self-modeling regression, differential equation models, and event-based models have been applied to provide a better understanding of disease progression patterns and biomarker trajectories. Additionally, the Cox-regression technique, Bayesian network models, and deep-learning-based approaches have been used to predict the probability of future incidence of disease, whereas nonnegative matrix factorization, nonhierarchical cluster analysis, hierarchical agglomerative clustering, and deep-learning-based approaches have been employed to stratify patients based on their disease subtypes. Furthermore, the interpretation of neurodegenerative disease data is possible through knowledge-based models which use prior knowledge to complement data-driven analyses. These knowledge-based models can include pathway-centric approaches to establish pathways perturbed in a given condition, as well as disease-specific knowledge maps, which elucidate the mechanisms involved in a given disease. Collectively, these established models have revealed high granular details and insights into neurodegenerative disease models.

SUMMARY

In conjunction with increasingly advanced computational approaches, a wide spectrum of neurodegenerative disease models, which can be broadly categorized into data-driven and knowledge-driven, have been developed. We review the state of the art data and knowledge-driven models and discuss the necessary steps which are vital to bring them into clinical application.

The role of disease duration and severity on novel clinical subtypes of Parkinson disease

INTRODUCTION

One of the latest subtyping systems of Parkinson disease (PD) identifies motor severity, cognitive dysfunction, dysautonomia, and rapid eye movement behavior disorder as key features for phenotyping patients into three different subtypes (i.e., mild motor-predominant, diffuse-malignant and intermediate). Since PD subtypes are clinically most relevant if they are mutually exclusive and consistent over-time, we explored the impact of disease stage and duration on these novel subtypes.

METHODS

One-hundred-twenty-two consecutive patients, with a disease duration ranging from 0 to 20 years, were allocated as suggested into these three subtypes. The relationship between either disease duration or stage, as measured by the Hoehn and Yahr staging, and subtype allocation was explored.

RESULTS

Significant differences in subtype distribution were observed across patients stratified according to either disease duration or staging, with the diffuse-malignant subtypes increasing in prevalence as the disease advanced. Both disease duration and staging were independent predictors of subtype allocation.

CONCLUSIONS

These novel PD subtypes are significantly influenced by disease duration and staging, which might suggest that they do not represent mutually exclusive disease pathways. This should be taken into account when attempting correlations with putative biomarkers of disease progression.

Is Parkinson's disease an unique clinical entity? Rigid or tremor dominant PD: Two faces of the same coin

Parkinson's disease is one of the most described neurodegenerative pathologies; though it is one of the most complex pathologies, is not fully understood, correctly identified, with its different types of presentation, its clinical course and the neural networks involved. We report on a series consisting of 432 de novo PD diagnosed patients, and 457 control cases. We identify a possible independent relationship between two clinical PD presentation, akinetic-rigid and tremor-dominant, and cognitive and behavioral changes. A 24-months follow-up allows to identify new information still not fully explored.

Subtypes of Sleep Disturbance in Parkinson's Disease Based on the Cross-Culturally Validated Korean Version of Parkinson's Disease Sleep Scale-2

BACKGROUND AND PURPOSE

This study aimed to determine the clinimetric properties of the Korean version of Parkinson's Disease Sleep Scale-2 (K-PDSS-2) and whether distinct subtypes of sleep disturbance can be empirically identified in patients with Parkinson's disease (PD) using the cross-culturally validated K-PDSS-2.

METHODS

The internal consistency, test-retest reliability, scale precision, and convergent validity of K-PDSS-2 were assessed in a nationwide, multicenter study of 122 patients with PD. Latent class analysis (LCA) was used to derive subgroups of patients who experienced similar patterns of sleep-related problems and nocturnal disabilities.

RESULTS

The total K-PDSS-2 score was 11.67±9.87 (mean±standard deviation) at baseline and 12.61±11.17 at the retest. Cronbach's α coefficients of the total K-PDSS-2 scores at baseline and follow-up were 0.851 and 0.880, respectively. The intraclass correlation coefficients over the 2-week study period ranged from 0.672 to 0.848. The total K-PDSS-2 score was strongly correlated with health-related quality of life measures and other corresponding nonmotor scales. LCA revealed three distinct subtypes of sleep disturbance in the study patients: "less-troubled sleepers," "PD-related nocturnal difficulties," and "disturbed sleepers."

CONCLUSIONS

K-PDSS-2 showed good clinimetric attributes in accordance with previous studies that employed the original version of the PDSS-2, therefore confirming the cross-cultural usefulness of the scale. This study has further documented the first application of an LCA approach for identifying subtypes of sleep disturbance in patients with PD.

Identification of different clinical faces of obstructive sleep apnea in children

OBJECTIVE

This study aimed to identify the heterogeneity of obstructive sleep apnea syndrome clinical presentation in children.

PARTICIPANTS

Children who were 3-14 years old and with obstructive sleep apnea syndrome after polysomnography monitoring (apnea and hypopnea index>5 or obstructive apnea index>1) in the sleep center of Beijing Children's Hospital were included.

METHODS

A sleep disorder questionnaire including different combinations of symptoms and co-morbidities of obstructive sleep apnea syndrome in children was used. A cluster analysis was used to classify the data.

RESULTS

The apnea hypopnea index alone is not adequate to predict clinical phenotypes. Based on symptoms and co-morbidities of obstructive sleep apnea syndrome, three distinct clusters were identified. They were "nocturnal snoring and daytime sleepiness group" (cluster 1), "hyperactivity group" (cluster 2), and "minimally symptomatic group" (cluster 3). A prediction model was built according to eight variables which showed statistical significance by pairwise comparison among clusters. Overall accuracy of the prediction model could reach 86%. Both the sensitivity and specificity of cluster 2 and 3 prediction were around 90%.

CONCLUSION

Children with obstructive sleep apnea syndrome have different patterns of clinical presentation and the identification of the different clinical profiles of obstructive sleep apnea syndrome can provide clues for more personalised diagnoses and therapies.

Parkinson's disease prognostic scores for progression of cognitive decline

Clinical and biochemical diversity of Parkinson’s disease (PD) presents a major challenge for accurate diagnosis and prediction of its progression. We propose, develop and optimize PD clinical scores as efficient integrated progression biomarkers for prediction of the likely rate of cognitive decline in PD patients. We considered 269 drug-naïve participants from the Parkinson’s Progression Marker Initiative database, diagnosed with idiopathic PD and observed between 4 and 6 years. Nineteen baseline clinical and pathological measures were systematically considered. Relative variable importance and logistic regressions were used to optimize combinations of significant baseline measures as integrated biomarkers. Parkinson’s disease cognitive decline scores were designed as new clinical biomarkers using optimally categorized baseline measures. Specificities and sensitivities of the biomarkers reached ~93% for prediction of severe rate of cognitive decline (with more than 5 points decline in 4 years on the Montreal Cognitive Assessment scale), and up to ~73% for mild-to-moderate decline (between 1 and 5 points decline). The developed biomarkers and clinical scores could resolve the long-standing clinical problem about reliable prediction of PD progression into cognitive deterioration. The outcomes also provide insights into the contributions of individual clinical and pathological measures to PD progression, and will assist with better-targeted treatment regiments, stratification of clinical trial and their evaluation.

Motor phenotype classification in moderate to advanced PD in BioFIND study

BACKGROUND

Three motor phenotypes have been described in PD: postural instability and gait difficulty (PIGD) dominant, tremor-dominant (TD), and indeterminate (IND) subtype. These phenotypes have been associated with different cognitive trajectories, motor outcomes, and biomarkers profiles. However, whether motor subtype classifications change with treatment and disease progression is not well established.

METHODS

To evaluate motor subtype ratio changes, we used the chi-square test for the off and on state motor subtypes for 115 PD participants in the BioFIND study and used repeated-measures analyses to evaluate longitudinal changes in 162 PD participants with five-year follow-up in the PPMI study.

RESULTS

PIGD and TD subtypes in moderate to advanced PD participants change with dopaminergic agents. For those who shifted subtypes, improvement in tremor accounted for the transition of 15 (25.4%) TD participants, while the lack of tremor improvement along with minimal changes in PIGD score resulted in changes for eight (19.0%) PIGD individuals. Analyses of PPMI data revealed that all three subgroups had a significant decrease in subtype ratio with disease progression and a significant decline in subtype ratio occurred only in the TD subgroup with dopaminergic agents. The impact of dopaminergic medication effect on subtype shift for each visit was also more notable with disease advancement.

CONCLUSIONS

Motor subtypes are not fixed but change with progression of the disease and with treatment. Improvement in tremor was the main contributor to motor phenotype transitions in the BioFIND cohort. A more stable classification system for subtypes based on underlying biological differences is desirable.

Identification of 6 dermatomyositis subgroups using principal component analysis-based cluster analysis

OBJECTIVE

Dermatomyositis (DM) is a heterogeneous disease with a wide range of clinical manifestations. The aim of the present study was to identify the clinical subtypes of DM by applying cluster analysis.

METHODS

We retrospectively reviewed the medical records of 720 DM patients and selected 21 variables for analysis, including clinical characteristics, laboratory findings, and comorbidities. Principal component analysis (PCA) was first conducted to transform the 21 variables into independent principal components. Patient classification was then performed using cluster analysis based on the PCA-transformed data. The relationships among the clinical variables were also assessed.

RESULTS

We transformed the 21 clinical variables into nine independent principal components by PCA and identified six distinct subgroups. Cluster A was composed of two sub-clusters of patients with classical DM and classical DM with minimal organ involvement. Cluster B patients were older and had malignancies. Cluster C was characterized by interstitial lung disease (ILD), skin ulcers, and minimal muscle involvement. Cluster D included patients with prominent lung, muscle, and skin involvement. Cluster E contained DM patients with other connective tissue diseases. Cluster F included all patients with myocarditis and prominent myositis and ILD. We found significant differences in treatment across the six clusters, with clusters E, C and D being more likely to receive aggressive immunosuppressive therapy.

CONCLUSION

We applied cluster analysis to a large group of DM patients and identified 6 clinical subgroups, underscoring the need for better phenotypic characterization to help develop individualized treatments and improve prognosis.

Non-motor Parkinson disease: new concepts and personalised management

Most patients with Parkinson disease (PD) have non-motor symptoms (NMS), and on average these can range from four to 19 different symptoms. NMS dominate the prodromal phase of PD and some may serve as clinical biomarkers of PD. NMS can be dopaminergic, non-dopaminergic, of genetic origin or drug induced. Clinical assessment of NMS should include the NMS Questionnaire (completed by patients) for screening, as recommended by the International Parkinson and Movement Disorders Society and other international societies. The total number of NMS in a patient with PD constitutes the NMS burden, which can be graded using validated cut-off scores on the NMS Questionnaire and Scale and can be used as an outcome measure in clinical trials. Despite NMS burden having a major effect on the quality of life of patients and carers, a large European study showed that NMS are often ignored in the clinic. The syndromic nature of PD is underpinned by non-motor subtypes which are likely to be related to specific dysfunction of cholinergic, noradrenergic, serotonergic pathways in the brain, not just the dopaminergic pathways. NMS can be treated by dopaminergic and non-dopaminergic strategies, but further robust studies supported by evidence from animal models are required. The future of modern treatment of PD needs to be supported by the delivery of personalised medicine.

Parkinson's Disease in the Era of Personalised Medicine: One Size Does Not Fit All

The concept of personalised medicine in Parkinson's disease has arrived where the implications of findings made in research are certain to have an increasing impact upon clinical practice. Disease heterogeneity in Parkinson's disease has been well described and lends itself to the construct of personalised medicine where it is hypothesised that a greater understanding of genetic and pathophysiological contributions may underpin the sub-groups described. This in turn has driven the development of potentially individualised disease-modifying therapies where, for example, we are beginning to see treatments that target patients with Parkinson's disease with specific genetic mutations. Furthermore, clinicians are increasingly recognising the need to tailor their management approach to patients depending on their age of presentation, acknowledging differential side-effect profiles and responses especially when considering the use of device-assisted technologies such as infusion or surgery. Clearly, individualising the treatment of both motor and non-motor symptoms will remain imperative but, in the future, personalised medicine may provide clearer insights into various aspects of a patient's symptomatology, disease course and thus the best therapeutic approaches.

Epigenetic Analysis in Human Neurons: Considerations for Disease Modeling in PD

Parkinson’s disease (PD) is the second most common neurodegenerative disorder next to Alzheimer’s disease. Most PD cases are considered to be sporadic and despite considerable scientific effort, the underlying cause(s) still remain(s) enigmatic. In particular, it is unknown to which extent epigenetic alterations contribute to the pathophysiology of this devastating disorder. This is partly due to the fact that appropriate PD models are not yet available. Moreover, epigenetic patterns and mechanisms are species specific and murine systems reflect only a few of the idiosyncrasies of human neurons. For several years now, patient-specific stem cell-derived neural and non-neural cells have been employed to overcome this limitation allowing the analysis and establishment of humanized disease models for PD. Thus, several studies tried to dissect epigenetic alterations such as aberrant DNA methylation or microRNA patterns using lund human mesencephalic cell lines or neurons derived from (patient-specific) induced pluripotent stem cells. These studies demonstrate that human neurons have the potential to be used as model systems for the study of epigenetic modifications in PD such as characterizing epigenetic changes, correlating epigenetic changes to gene expression alterations and hopefully using these insights for the development of novel therapeutics. However, more research is required to define the epigenetic (age-associated) landscape of human in vitro neurons and compare these to native neurons before they can be established as suitable models for epigenetic studies in PD. In this review, we summarize the knowledge about epigenetic studies performed on human neuronal PD models, and we discuss advantages and current limitations of these (stem cell-derived) neuronal models for the study of epigenetic alterations in PD.

Applying Machine Learning Algorithms to Segment High-Cost Patient Populations

BACKGROUND

Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients.

OBJECTIVE

To evaluate the performance of three different machine learning algorithms for identifying subgroups of high-cost patients.

DESIGN

We applied three different clustering algorithms-connectivity-based clustering using agglomerative hierarchical clustering, centroid-based clustering with the k-medoids algorithm, and density-based clustering with the OPTICS algorithm-to a clinical and administrative dataset. We then examined the extent to which each algorithm identified subgroups of patients that were (1) clinically distinct and (2) associated with meaningful differences in relevant utilization metrics.

PARTICIPANTS

Patients enrolled in a national Medicare Advantage plan, categorized in the top decile of spending (n = 6154).

MAIN MEASURES

Post hoc discriminative models comparing the importance of variables for distinguishing observations in one cluster from the rest. Variance in utilization and spending measures.

KEY RESULTS

Connectivity-based, centroid-based, and density-based clustering identified eight, five, and ten subgroups of high-cost patients, respectively. Post hoc discriminative models indicated that density-based clustering subgroups were the most clinically distinct. The variance of utilization and spending measures was the greatest among the subgroups identified through density-based clustering.

CONCLUSIONS

Machine learning algorithms can be used to segment a high-cost patient population into subgroups of patients that are clinically distinct and associated with meaningful differences in utilization and spending measures. For these purposes, density-based clustering with the OPTICS algorithm outperformed connectivity-based and centroid-based clustering algorithms.

Developing and validating Parkinson's disease subtypes and their motor and cognitive progression

OBJECTIVES

To use a data-driven approach to determine the existence and natural history of subtypes of Parkinson's disease (PD) using two large independent cohorts of patients newly diagnosed with this condition.

METHODS

1601 and 944 patients with idiopathic PD, from Tracking Parkinson's and Discovery cohorts, respectively, were evaluated in motor, cognitive and non-motor domains at the baseline assessment. Patients were recently diagnosed at entry (within 3.5 years of diagnosis) and were followed up every 18 months. We used a factor analysis followed by a k-means cluster analysis, while prognosis was measured using random slope and intercept models.

RESULTS

We identified four clusters: (1)  fast motor progression with symmetrical motor disease, poor olfaction, cognition and postural hypotension; (2) mild motor and non-motor disease with intermediate motor progression; (3) severe motor disease, poor psychological well-being and  poor sleep with an intermediate motor progression; (4) slow motor progression with tremor-dominant, unilateral disease. Clusters were moderately to substantially stable across the two cohorts (kappa 0.58). Cluster 1 had the fastest motor progression in Tracking Parkinson's at 3.2 (95% CI 2.8 to 3.6) UPDRS III points per year while cluster 4 had the slowest at 0.6 (0.1-1.1). In Tracking Parkinson's, cluster 2 had the largest response to levodopa 36.3% and cluster 4 the lowest 28.8%.

CONCLUSIONS

We have found four novel clusters that replicated well across two independent early PD cohorts and were associated with levodopa response and motor progression rates. This has potential implications for better understanding disease pathophysiology and the relevance of patient stratification in future clinical trials.

A novel, data-driven conceptualization for critical left heart obstruction

BACKGROUND

Qualitative features of aortic and mitral valvar pathology have traditionally been used to classify congenital cardiac anomalies for which the left heart structures are unable to sustain adequate systemic cardiac output. We aimed to determine if novel groups of patients with greater clinical relevance could be defined within this population of patients with critical left heart obstruction (CLHO) using a data-driven approach based on both qualitative and quantitative echocardiographic measures.

METHODS

An independent standardized review of recordings from pre-intervention transthoracic echocardiograms for 651 neonates with CLHO was performed. An unsupervised cluster analysis, incorporating 136 echocardiographic measures, was used to group patients with similar characteristics. Key measures differentiating the groups were then identified.

RESULTS

Based on all measures, cluster analysis linked the 651 neonates into groups of 215 (Group 1), 338 (Group 2), and 98 (Group 3) patients. Aortic valve atresia and left ventricular (LV) end diastolic volume were identified as significant variables differentiating the groups. The median LV end diastolic area was 1.35, 0.69, and 2.47 cm² in Groups 1, 2, and 3, respectively (p < 0.0001). Aortic atresia was present in 11% (24/215), 87% (294/338), and 8% (8/98), in Groups 1, 2, and 3, respectively (p < 0.0001). Balloon aortic valvotomy was the first intervention for 9% (19/215), 2% (6/338), and 61% (60/98), respectively (p < 0.0001). For those with an initial operation, single ventricle palliation was performed in 90% (176/215), 98% (326/338), and 58% (22/38) (p < 0.0001). Overall mortality in each group was 27% (59/215), 41% (138/338), and 12% (12/98) (p < 0.0001).

CONCLUSIONS

Using a data-driven approach, we conceptualized three distinct patient groups, primarily based quantitatively on baseline LV size and qualitatively by the presence of aortic valve atresia. Management strategy and overall mortality differed significantly by group. These groups roughly correspond anatomically and are analogous to multi-level LV hypoplasia, hypoplastic left heart syndrome, and critical aortic stenosis, respectively. Our analysis suggests that quantitative and qualitative assessment of left heart structures, particularly LV size and type of aortic valve pathology, may yield conceptually more internally consistent groups than a simplistic scheme limited to valvar pathology alone.

Fatigue in Parkinson's disease: A systematic review and meta-analysis

We conducted a systematic review and meta-analysis aimed at establishing robust prevalence estimates and identifying clinical correlates of fatigue in PD. From 2,459 titles and abstracts, we selected 44 relevant studies (n = 7427 patients). Overall, the meta-analysis showed a prevalence of fatigue of 50% in PD. This prevalence estimate, however, was significantly moderated by study heterogeneity in measurement scales and cut-off thresholds. In contrast, demographic features, disease severity, cognitive impairment, and depression did not moderate prevalence estimates. Moreover, fatigue prevalence did not differ between de novo and treated PD patients. Compared to nonfatigued patients, fatigued patients had sligthly higher age (1.44 years), disease duration (0.93 years), l-dopa equivalent daily dose (50.89 units), UPDRS-III (4.99 points), and H & Y (0.33 points), as well as risk of comorbid depression (risk ratio = 1.89) and had a little lower MMSE score (-0.66 points). Fatigue was moderately associated with apathy (Hedges' g = 0.55), anxiety (Hedges' g = 0.67), daytime somnolence (Hedges' g = 0.43), sleep disturbances (Hedges' g = 0.66), and poorer quality of life (Hedges' g = 1.23). Our analyses suggest that fatigue is a frequent, independent nonmotor symptom in PD appearing early and persisting throughout the disease course, and that establishing uniform diagnostic criteria for PD-related fatigue is critical. In addition, several nonmotor symptoms appear to be associated with fatigue and negatively impact quality of life. Pharmacological and nonpharmacological interventions targeting fatigue and associated symptoms may improve quality of life in patients with PD. © 2018 International Parkinson and Movement Disorder Society.

The presence of depression in de novo Parkinson's disease reflects poor motor compensation

Depression frequently accompanies Parkinson's disease and often precedes the onset of motor symptoms. This study aimed to evaluate the impact of depression on motor compensation in patients with de novo Parkinson's disease. This retrospective cohort study analyzed data from 474 non-demented patients with de novo Parkinson's disease (mean age, 64.6±9.8 years; 242 men) who underwent both dopamine transporter PET scan and depression assessment using the Beck Depression Inventory at baseline. Patients were classified into tertiles by Beck Depression Inventory score. At baseline, high-tertile group (Beck Depression Inventory score ≥15, n = 157) showed more severe motor deficits and lower cognitive function than low-tertile group (Beck Depression Inventory score ≤7, n = 158, P = 0.034 and P = 0.008, respectively). Greater motor deficits in high-tertile group than low-tertile group remained significant after controlling for dopamine transporter binding in the posterior putamen, as well as other confounding variables. During follow-up of a median duration of 47 months, high-tertile group received higher levodopa-equivalent doses for symptom control than did low-tertile group after controlling for age, gender, and initial motor deficit severity. These results demonstrate that depression in de novo Parkinson's disease is associated with motor deficit severity at baseline and dose of PD medications during follow-up, suggesting that the presence of depression in de novo Parkinson's disease represents poor motor compensation.

Deep Brain Stimulation and L-DOPA Therapy: Concepts of Action and Clinical Applications in Parkinson's Disease

L-DOPA is still the most effective pharmacological therapy for the treatment of motor symptoms in Parkinson's disease (PD) almost four decades after it was first used. Deep brain stimulation (DBS) is a safe and highly effective treatment option in patients with PD. Even though a clear understanding of the mechanisms of both treatment methods is yet to be obtained, the combination of both treatments is the most effective standard evidenced-based therapy to date. Recent studies have demonstrated that DBS is a therapy option even in the early course of the disease, when first complications arise despite a rigorous adjustment of the pharmacological treatment. The unique feature of this therapeutic approach is the ability to preferentially modulate specific brain networks through the choice of stimulation site. The clinical effects have been unequivocally confirmed in recent studies; however, the impact of DBS and the supplementary effect of L-DOPA on the neuronal network are not yet fully understood. In this review, we present emerging data on the presumable mechanisms of DBS in patients with PD and discuss the pathophysiological similarities and differences in the effects of DBS in comparison to dopaminergic medication. Targeted, selective modulation of brain networks by DBS and pharmacodynamic effects of L-DOPA therapy on the central nervous system are presented. Moreover, we outline the perioperative algorithms for PD patients before and directly after the implantation of DBS electrodes and strategies for the reduction of side effects and optimization of motor and non-motor symptoms.

Analysis of the clinical features of early Parkinson's disease with comparatively integrated intestinal function

OBJECTIVE

Constipation is among the most frequently delineated nonmotor symptoms (NMS) with a high occurrence in Parkinson's disease (PD). The purpose of our study was to investigate whether PD with comparatively integrated intestinal function (without constipation) in the early stage had different clinical features compared to constipated PD.

METHOD

We conducted a study of 105 consecutive de novo as well as early treated (treated for shorter than 3 months), aged 50 years or older outpatients. Subjects were administered motor and nonmotor questionnaires as well as constipation associated examinations. Then, we explored the distinctive features of nonconstipated contrasted to constipated PD by using univariate, multiple regression analysis and correlation analysis.

RESULTS

Nonconstipated PD tended to have fewer motor deficits, as well as lower Hoehn and Yahr (H&Y) stage and they mainly presented as tremor-dominant (TD), while constipated group had a higher occurrence of posture instability and gait difficulty (PIGD); nonconstipated patients were inclined to live in urban area, the NMSloads and prevalence of NMS were lower compared to constipated ones. Correlation analysis found a discord between NMSloads and disease severity based on H&Y stage and motor scores in nonconstipated PD.

CONCLUSIONS

These results suggest that PD without constipation in early stage may represent a unique clinical phenotype, which may be more benign than PD with constipation.

Objective measures of unobstructed walking and obstacle avoidance in Parkinson's disease subtypes

BACKGROUND

Objective measures of gait in Parkinson's disease (PD) patients according to motor subtypes are not yet fully understood. Although recent advances have been made for unobstructed walking, further work is required on locomotor tasks challenging postural stability, such as obstacle avoidance.

RESEARCH QUESTION

This study aimed to investigate the influence of PD motor subtypes on objective measures of locomotion during unobstructed walking and obstacle avoidance.

METHODS

Thirty-five PD patients classified as postural instability and gait disorder (PIGD) and 30 as tremor dominant (TD), as well as 45 healthy controls (CG) walked along an 8-m pathway under two conditions: (a) unobstructed walking and (b) obstacle avoidance. Outcome measures included spatiotemporal parameters recorded by an optoelectronic tridimensional system.

RESULTS

During unobstructed walking, the PIGD group exhibited shorter stride length, slower velocity, and longer double support phase compared to the TD and CG groups. The TD group also presented slower stride velocity compared to the CG. The PIGD and TD groups presented shorter stride duration than the CG. Regarding obstacle avoidance, the PIGD group exhibited shorter distances for leading foot placement before obstacle, trailing foot placement after obstacle and trailing crossing step length compared to the TD and CG groups. The PIGD group exhibited wider leading crossing step width, lower trailing toe clearance, and slower leading and trailing velocity during obstacle avoidance compared to the CG.

SIGNIFICANCE

PIGD subtype patients showed worse modifications in objective measures of unobstructed walking and obstacle avoidance. The observed modifications may contribute to increased fall occurrence in PIGD patients.

Classification of Pediatric Asthma: From Phenotype Discovery to Clinical Practice

Advances in big data analytics have created an opportunity for a step change in unraveling mechanisms underlying the development of complex diseases such as asthma, providing valuable insights that drive better diagnostic decision-making in clinical practice, and opening up paths to individualized treatment plans. However, translating findings from data-driven analyses into meaningful insights and actionable solutions requires approaches and tools which move beyond mining and patterning longitudinal data. The purpose of this review is to summarize recent advances in phenotyping of asthma, to discuss key hurdles currently hampering the translation of phenotypic variation into mechanistic insights and clinical setting, and to suggest potential solutions that may address these limitations and accelerate moving discoveries into practice. In order to advance the field of phenotypic discovery, greater focus should be placed on investigating the extent of within-phenotype variation. We advocate a more cautious modeling approach by "supervising" the findings to delineate more precisely the characteristics of the individual trajectories assigned to each phenotype. Furthermore, it is important to employ different methods within a study to compare the stability of derived phenotypes, and to assess the immutability of individual assignments to phenotypes. If we are to make a step change toward precision (stratified or personalized) medicine and capitalize on the available big data assets, we have to develop genuine cross-disciplinary collaborations, wherein data scientists who turn data into information using algorithms and machine learning, team up with medical professionals who provide deep insights on specific subjects from a clinical perspective.

Metabotyping for the development of tailored dietary advice solutions in a European population: the Food4Me study

Traditionally, personalised nutrition was delivered at an individual level. However, the concept of delivering tailored dietary advice at a group level through the identification of metabotypes or groups of metabolically similar individuals has emerged. Although this approach to personalised nutrition looks promising, further work is needed to examine this concept across a wider population group. Therefore, the objectives of this study are to: (1) identify metabotypes in a European population and (2) develop targeted dietary advice solutions for these metabotypes. Using data from the Food4Me study (n 1607), k-means cluster analysis revealed the presence of three metabolically distinct clusters based on twenty-seven metabolic markers including cholesterol, individual fatty acids and carotenoids. Cluster 2 was identified as a metabolically healthy metabotype as these individuals had the highest Omega-3 Index (6·56 (sd 1·29) %), carotenoids (2·15 (sd 0·71) µm) and lowest total saturated fat levels. On the basis of its fatty acid profile, cluster 1 was characterised as a metabolically unhealthy cluster. Targeted dietary advice solutions were developed per cluster using a decision tree approach. Testing of the approach was performed by comparison with the personalised dietary advice, delivered by nutritionists to Food4Me study participants (n 180). Excellent agreement was observed between the targeted and individualised approaches with an average match of 82 % at the level of delivery of the same dietary message. Future work should ascertain whether this proposed method could be utilised in a healthcare setting, for the rapid and efficient delivery of tailored dietary advice solutions.

Parkinson's Disease Subtypes Identified from Cluster Analysis of Motor and Non-motor Symptoms

Parkinson's disease is now considered a complex, multi-peptide, central, and peripheral nervous system disorder with considerable clinical heterogeneity. Non-motor symptoms play a key role in the trajectory of Parkinson's disease, from prodromal premotor to end stages. To understand the clinical heterogeneity of Parkinson's disease, this study used cluster analysis to search for subtypes from a large, multi-center, international, and well-characterized cohort of Parkinson's disease patients across all motor stages, using a combination of cardinal motor features (bradykinesia, rigidity, tremor, axial signs) and, for the first time, specific validated rater-based non-motor symptom scales. Two independent international cohort studies were used: (a) the validation study of the Non-Motor Symptoms Scale (n = 411) and (b) baseline data from the global Non-Motor International Longitudinal Study (n = 540). k-means cluster analyses were performed on the non-motor and motor domains (domains clustering) and the 30 individual non-motor symptoms alone (symptoms clustering), and hierarchical agglomerative clustering was performed to group symptoms together. Four clusters are identified from the domains clustering supporting previous studies: mild, non-motor dominant, motor-dominant, and severe. In addition, six new smaller clusters are identified from the symptoms clustering, each characterized by clinically-relevant non-motor symptoms. The clusters identified in this study present statistical confirmation of the increasingly important role of non-motor symptoms (NMS) in Parkinson's disease heterogeneity and take steps toward subtype-specific treatment packages.

Past, present, and future of Parkinson's disease: A special essay on the 200th Anniversary of the Shaking Palsy

This article reviews and summarizes 200 years of Parkinson's disease. It comprises a relevant history of Dr. James Parkinson's himself and what he described accurately and what he missed from today's perspective. Parkinson's disease today is understood as a multietiological condition with uncertain etiopathogenesis. Many advances have occurred regarding pathophysiology and symptomatic treatments, but critically important issues are still pending resolution. Among the latter, the need to modify disease progression is undoubtedly a priority. In sum, this multiple-author article, prepared to commemorate the bicentenary of the shaking palsy, provides a historical state-of-the-art account of what has been achieved, the current situation, and how to progress toward resolving Parkinson's disease. © 2017 International Parkinson and Movement Disorder Society.

Merging Clinical and Imaging Biomarkers to Tackle Parkinson's Disease

Background

In Parkinson's disease, biomarkers represent tools that are potentially suitable for either clinical or research settings and are useful in predicting onset, confirming diagnosis, detecting progression, and evaluating response to potential disease-modifying treatments. The range of available biomarkers in Parkinson's disease is fast expanding and includes an increasing amount of laboratory, clinical, and imaging data. Indeed, the latter 2 represent the cornerstones of the diagnostic criteria for Parkinson's disease recently proposed by the International Parkinson and Movement Disorders Society Task Force on the definition of Parkinson's disease.

Methods and Results

In this review, we describe current knowledge and emerging findings on clinical (with emphasis on nonmotor symptoms) and imaging biomarkers for Parkinson's disease, with a focus on prodromal, diagnostic, and middle/advanced phases.

Conclusion

An increasing body of evidence suggests that merging clinical and imaging biomarkers through disease stages may be the best, fastest track to tackle Parkinson's disease.

Frontal Tasks and Behavior in Rigid or Tremor-Dominant Parkinson Disease

Parkinson disease (PD) is not an unambiguous entity, and there is a general consensus for the statement that an akinetic-rigid dominant type of presentation has a worse prognosis, in the follow-up. The aim of our study was to examine the differences in frontal tasks and behavior, in 2 PD naive groups: the rigid and the tremor-dominant types of presentation, according to motor scores. Our study has showed some important differences in frontal tasks and in behavior, performing more apathy, aggressiveness, and irritability in the rigid type, and more depression and anxiety in the tremor-dominant type. The former group causes the caregiver more distress and has a very rapid disease progression. It can be argued that rigid type PD presentation needs specific dedicated cares and more strong clinical attention.

Motor subtype changes in early Parkinson's disease

INTRODUCTION

Distinct motor subtypes of Parkinson's disease (PD) have been described through both clinical observation and through data-driven approaches. However, the extent to which motor subtypes change during disease progression remains unknown. Our objective was to determine motor subtypes of PD using an unsupervised clustering methodology and evaluate subtype changes with disease duration.

METHODS

The Parkinson's Progression Markers Initiative database of 423 newly diagnosed PD patients was utilized to retrospectively identify unique motor subtypes through a data-driven, hierarchical correlational clustering approach. For each patient, we assigned a subtype to each motor assessment at each follow-up visit (time points) and by using published criteria. We examined changes in PD subtype with disease duration using both qualitative and quantitative methods.

RESULTS

Five distinct motor subtypes were identified based on the motor assessment items and these included: Tremor Dominant (TD), Axial Dominant, Appendicular Dominant, Rigidity Dominant, and Postural and Instability Gait Disorder Dominant. About half of the patients had consistent subtypes at all time points. Most patients met criteria for TD subtype soon after diagnosis. For patients with inconsistent subtypes, there was an overall trend to shift away from a TD phenotype with disease duration, as shown by chi-squared test, p < 0.001, and linear regression analysis, p < 0.05.

CONCLUSION

These results strongly suggest that classification of motor subtypes in PD can shift with increasing disease duration. Shifting subtypes is a factor that should be accounted for in clinical practice or in clinical trials.

Quality of Life and Nonmotor Symptoms in Parkinson's Disease

Health-related quality of life (HRQoL) is defined as "the perception and evaluation by patients themselves of the impact caused on their lives by the disease and its consequences." HRQoL is conceptualized as a combination of physical, psychological, and social well-being in the context of a particular disease. Following earlier studies revolving on the impact of the classic motor symptoms of Parkinson's disease on HRQoL, mounting evidence have been produced that nonmotor symptoms (NMS) significantly and independently contribute to worse HRQoL. This holds particularly true for such NMS such as neuropsychiatric disturbances, cognitive impairment, and fatigue, the burden of which might well exceed the effects of the motor symptoms. Nonetheless, there is very sparse evidence on how to manage these NMS and whether targeting NMS would in fact lead to an improvement of HRQoL, which calls for the need of future trials with NMS as primary outcomes.

Nonmotor Subtyping in Parkinson's Disease

Nonmotor symptoms are integral to Parkinson's disease. Several subtypes dominated by specific nonmotor symptoms have emerged. In this chapter, the rationale behind nonmotor subtyping and currently proposed nonmotor subgroups within Parkinson's disease based on data-driven cluster analysis and clinical observations will be summarized. Furthermore, the concept of seven clinical nonmotor subtypes will be discussed in detail including the clinical presentation, potential biomarkers, and the clinical relevance. In future, nonmotor subtypes will possibly play a major role within the aim to achieve personalized medicine.

The Epidemiology of Nonmotor Symptoms in Parkinson's Disease (Cohort and Other Studies)

Nonmotor symptoms (NMS) of Parkinson's disease (PD) were recognized by James Parkinson himself and are now considered to be an integral part of PD. While clinical assessment had focused on prevalence and severity of individual NMS such as dementia and depression, work in the last decade has concentrated on global or holistic assessment of NMS using validated tools such as the NMS questionnaire and NMS scale. These studies from cohorts of varying sizes have allowed comparison of NMS across different disease stages, duration, age, and ethnicity in PD. The data also allow exploration of the concept of the nonlinear relationship of NMS to disease duration of PD and motor stages as well as nonmotor subtypes of PD. In this chapter, these aspects of epidemiological studies of NMS in PD cohorts are described.

Weight in Parkinson's Disease: Phenotypical Significance

Body weight in Parkinson's disease (PD) is a significant nonmotor feature. Weight homeostasis is a complex physiological process and gets deranged in PD patients leading to changes in weight. While both the low and high body weight have been reported as risk factors for PD, the majority of PD patients have a lower weight and a subset of patients lose weight during the course of the disease, while a small proportion gain weight. A number of clinical parameters such as older age, impaired cognition, severity of disease, and an imbalance of food intake determined by satiety and hunger hormones have been reported to be associated with but not the cause of weight change. Low body weight and weight loss have a negative impact on disease severity, dyskinesia quality of life, and mortality indicative of disease progression. An early assessment of olfactory impairment seems to identify patients at risk of weight loss, the patients with more severe olfactory loss-anosmic group, lose weight as compared to the patients with some preservation of olfaction, the hyposmic group. Higher levodopa dose per kilogram body weight increases the risk of dyskinesia, higher body weight seems to be protective against this complication. The identification of PD patients according to the nonmotor phenotype of "Park-olfaction-weight-phenotype" and the "olfaction-weight-dyskinesia" triad should help to develop strategies to prevent weight reduction and improve general health and complications of PD patients. The phenotype seems to reflect a differential prodromal pathology and influence clinical disease. Higher body weight patients would benefit from life style changes to achieve a healthy profile. Weight monitoring and weight orientated approach to management of PD patients should help to improve their outcome. Body weight change might be a surrogate to disease progression and may be used to investigate neuroprotection strategies.

Advances in the Clinical Differential Diagnosis of Parkinson's Disease

The differential diagnosis of Parkinson's disease has widened considerably in recent years. This chapter aims to summarize the current knowledge on the clinical differential diagnoses of sporadic Parkinson's disease. As the number of monogenic familial Parkinson's disease variants and risk factors is growing, so is the number of appreciated etiologies of atypical parkinsonian and other pallidopyramidal syndromes. This work aims at summarizing the current knowledge on both motor and nonmotor neurological signs and symptoms that aid the clinical diagnosis of Parkinson's disease and its differential diagnoses.

The PRIAMO study: urinary dysfunction as a marker of disease progression in early Parkinson's disease

BACKGROUND AND PURPOSE

New venues are currently being explored to predict disease progression in Parkinson's disease (PD), such as non-motor subtypes and models merging motor and non-motor symptoms (NMS). By involving a subgroup of 585 patients from the PRIAMO (Parkinson Disease Non-motor Symptoms) study, the present 24-month longitudinal prospective analysis aimed to demonstrate that urinary dysfunction is an early marker of higher motor and non-motor burden as well as lower health-related quality of life.

METHODS AND RESULTS

Multivariable mixed-effect logistic regression models controlling for demographic and clinical variables showed that the following NMS domains were associated with urinary dysfunction: gastrointestinal [odds ratio (OR) 2.57, 95% confidence interval (CI) 1.67-3.97, P < 0.001], cardiovascular (OR 2.22, 95% CI 1.18-4.17, P = 0.013), skin (OR 1.81, 95% CI 1.06-3.08, P = 0.029), sleep (OR 2.06, 95% CI 1.34-3.16, P = 0.001), pain (OR 1.85, 95% CI 1.21-2.83, P = 0.004), fatigue (OR 2.40, 95% CI 1.56-3.68, P < 0.001), apathy (OR 2.79, 95% CI 1.72-4.52, P < 0.001) and respiratory (OR 1.82, 95% CI 1.02-3.23, P = 0.039). Analysis also demonstrated that urinary dysfunction was associated with higher motor disability (coefficient 1.73, 95% CI 0.68-2.78, P = 0.001) and lower health-related quality of life (coefficient -0.05, 95% CI -0.08 to -0.02, P < 0.001, and coefficient -3.49, 95% CI -5.21 to -1.77, P < 0.001) but not with more severe cognitive disability (coefficient -0.34, 95% CI -0.92 to 0.24, P = 0.251).

CONCLUSIONS

This is the first prospective longitudinal study involving a large cohort of PD patients demonstrating the relevance of urinary dysfunction as an early marker of higher motor and non-motor disability as well as lower health-related quality of life. These findings support a role for urinary dysfunction as an early marker of more severe disease progression.

Non-human primate models of PD to test novel therapies

Non-human primate (NHP) models of Parkinson disease show many similarities with the human disease. They are very useful to test novel pharmacotherapies as reviewed here. The various NHP models of this disease are described with their characteristics including the macaque, the marmoset, and the squirrel monkey models. Lesion-induced and genetic models are described. There is no drug to slow, delay, stop, or cure Parkinson disease; available treatments are symptomatic. The dopamine precursor, L-3,4-dihydroxyphenylalanine (L-Dopa) still remains the gold standard symptomatic treatment of Parkinson. However, involuntary movements termed L-Dopa-induced dyskinesias appear in most patients after chronic treatment and may become disabling. Dyskinesias are very difficult to manage and there is only amantadine approved providing only a modest benefit. In this respect, NHP models have been useful to seek new drug targets, since they reproduce motor complications observed in parkinsonian patients. Therapies to treat motor symptoms in NHP models are reviewed with a discussion of their translational value to humans. Disease-modifying treatments tested in NHP are reviewed as well as surgical treatments. Many biochemical changes in the brain of post-mortem Parkinson disease patients with dyskinesias are reviewed and compare well with those observed in NHP models. Non-motor symptoms can be categorized into psychiatric, autonomic, and sensory symptoms. These symptoms are present in most parkinsonian patients and are already installed many years before the pre-motor phase of the disease. The translational usefulness of NHP models of Parkinson is discussed for non-motor symptoms.

The Nonmotor Features of Parkinson's Disease

Nonmotor symptoms (NMS) of Parkinson's disease (PD) were recognized by the great James Parkinson himself who mentioned symptoms such as sleep dysfunction, delirium, dementia, and dysautonomia, in his seminal 1817 essay, "An Essay on the Shaking Palsy" (Parkinson, 1817). In spite of the key impact of PD NMS on quality of life, there was little holistic research and awareness till the validation and use of comprehensive tools such as the NMS questionnaire, scale, and the revised version of the unified PD rating scale. Research studies using these tools highlighted the key impact of the burden of NMS on quality of life of PD patients and the need for NMS to be routinely assessed in clinic. We now define PD as a motor and nonmotor disorder, and the natural history includes a long prodromal phase of PD dominated by a range of NMS. The prodromal phase is the subject of much research particularly in relation to neuroprotection and identifying subjects at risk. Use of NMS tools has also validated burden grading of NMS with cutoff values, which can be used as outcome measure in clinical trials. Finally, the complex multineurotransmitter dysfunction that is seen in PD has been shown to manifest clinically as nonmotor subtypes. Recognition of such subtypes is likely to lead to the emergence of personalized and precision medicine in PD.

Rivastigmine as a Symptomatic Treatment for Apathy in Parkinson's Dementia Complex: New Aspects for This Riddle

Over 90% of PDD patients show at least one neuropsychiatric symptom (NPS); in the 60-70% two or more NPS are present. Their incidence is important in terms of prognosis and severity of pathology. However, among all NPS, apathy is often the most disturbing, associated with greater caregiver's burden. Similar to other NPS, apathy may be due to a dysfunction of the nigrostriatal pathway, even though, not all the PD patients become apathetic, indicating that apathy should not entirely be considered a dopamine-dependent syndrome, and in fact it might also be related to acetylcholine defects. Apathy has been treated in many ways, without sure benefits; among these, Rivastigmine may present benefic properties. We present a series of 48 patients, suffering from PDD, treated with Rivastigmine, and followed-up for one year; they have been devotedly studied for apathy, even though all the other NPS disorders have been registered. Rivastigmine did not have a prolonged benefic effect on apathy, in our work, on the contrary of what had been observed in the literature, probably due to the longer follow-up of our patients.