Patterns of Motor and Non-Motor Features in Medication-Naïve Parkinsonism

Jain, Samay; Park, Seo-Young; Comer, Diane

doi:10.1159/000437228

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…In contrast to the lack of clusters in our motor FND group of patients, previous high-quality studies using the same methodology (gap statistics) reported homogeneous clusters including drug-naive parkinsonism (Jain, Park, & Comer, 2015), comorbidities associated with obesity (Reategui, Ratte, Bautista-Valarezo, & Duque, 2019), breast cancer progression data (Alexe, Dalgin, Ganesan, Delisi, & Bhanot, 2007). However, most cluster analysis studies in neurological conditions with motor symptoms such as Parkinson's disease (Ba, Obaid, Wieler, Camicioli, & Martin, 2016; Mu et al, 2017; Yang, Kim, Yun, Kim, & Jeon, 2014) or fibromyalgia (Yim et al, 2017) suffered from important methodological problems which could have led to false-positive cluster identification.…”

Section: Cluster Analysiscontrasting

confidence: 77%

The complex syndrome of functional neurological disorder

et al. 2022

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Background Patients with functional neurological disorders (FND) often present with multiple motor, sensory, psychological and cognitive symptoms. In order to explore the relationship between these common symptoms, we performed a detailed clinical assessment of motor, non-motor symptoms, health-related quality of life (HRQoL) and disability in a large cohort of patients with motor FND. To understand the clinical heterogeneity, cluster analysis was used to search for subgroups within the cohort. Methods One hundred fifty-two patients with a clinically established diagnosis of motor FND were assessed for motor symptom severity using the Simplified Functional Movement Disorder Rating Scale (S-FMDRS), the number of different motor phenotypes (i.e. tremor, dystonia, gait disorder, myoclonus, and weakness), gait severity and postural instability. All patients then evaluated each motor symptom type severity on a Likert scale and completed questionnaires for depression, anxiety, pain, fatigue, cognitive complaints and HRQoL. Results Significant correlations were found among the self-reported and all objective motor symptoms severity measures. All self-reported measures including HRQoL correlated strongly with each other. S-FMDRS weakly correlated with HRQoL. Hierarchical cluster analysis supplemented with gap statistics revealed a homogenous patient sample which could not be separated into subgroups. Conclusions We interpret the lack of evidence of clusters along with a high degree of correlation between all self-reported and objective measures of motor or non-motor symptoms and HRQoL within current neurobiological models as evidence to support a unified pathophysiology of ‘functional’ symptoms. Our results support the unification of functional and somatic syndromes in classification schemes and for future mechanistic and therapeutic research.

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Section: Cluster Analysiscontrasting

confidence: 77%

The complex syndrome of functional neurological disorder

et al. 2022

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“…Both features also significantly differed between early PD and SWEDD but not early PD and controls (see Tables 1, 2). These findings, in concert with other PPMI data research (40,89), warrant further investigation. Is the difference in years of education, fewer years of education in SWEDD, just specific to the particular SWEDD cohort used?…”

Section: Discussionsupporting

confidence: 57%

Non-motor Clinical and Biomarker Predictors Enable High Cross-Validated Accuracy Detection of Early PD but Lesser Cross-Validated Accuracy Detection of Scans Without Evidence of Dopaminergic Deficit

2020

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Background: Early stage (preclinical) detection of Parkinson's disease (PD) remains challenged yet is crucial to both differentiate it from other disorders and facilitate timely administration of neuroprotective treatment as it becomes available. Objective: In a cross-validation paradigm, this work focused on two binary predictive probability analyses: classification of early PD vs. controls and classification of early PD vs. SWEDD (scans without evidence of dopamine deficit). It was hypothesized that five distinct model types using combined non-motor and biomarker features would distinguish early PD from controls with > 80% cross-validated (CV) accuracy, but that the diverse nature of the SWEDD category would reduce early PD vs. SWEDD CV classification accuracy and alter model-based feature selection. Methods: Cross-sectional, baseline data was acquired from the Parkinson's Progressive Markers Initiative (PPMI). Logistic regression, general additive (GAM), decision tree, random forest and XGBoost models were fitted using non-motor clinical and biomarker features. Randomized train and test data partitions were created. Model classification CV performance was compared using the area under the curve (AUC), sensitivity, specificity and the Kappa statistic. Results: All five models achieved >0.80 AUC CV accuracy to distinguish early PD from controls. The GAM (CV AUC 0.928, sensitivity 0.898, specificity 0.897) and XGBoost (CV AUC 0.923, sensitivity 0.875, specificity 0.897) models were the top classifiers. Performance across all models was consistently lower in the early PD/SWEDD analyses, where the highest performing models were XGBoost (CV AUC 0.863, sensitivity 0.905, specificity 0.748) and random forest (CV AUC 0.822, sensitivity 0.809, specificity 0.721). XGBoost detection of non-PD SWEDD matched 1-2 years curated diagnoses in 81.25% (13/16) cases. In both early PD/control and early PD/SWEDD analyses, and across Interpretation: Non-motor clinical and biomarker variables enable high CV discrimination of early PD vs. controls but are less effective discriminating early PD from SWEDD.

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“…Given his interests, it is not surprising that Samay wrote extensively about nonmotor and autonomic features of PD. Indeed, he performed both original clinical laboratory‐based research and epidemiological studies of these aspects of the disease, and he also wrote well‐cited reviews . In 2012, together with David Goldstein, Samay guest edited a special issue of Neurobiology of Disease that was dedicated to nonmotor aspects of PD.…”

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confidence: 99%

Samay Jain, MD, June 2, 1974-September 8, 2016

Greenamyre

2016

Mov Disord.

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Patterns of Motor and Non-Motor Features in Medication-Naïve Parkinsonism

Cited by 9 publications

References 27 publications

The complex syndrome of functional neurological disorder

The complex syndrome of functional neurological disorder

Non-motor Clinical and Biomarker Predictors Enable High Cross-Validated Accuracy Detection of Early PD but Lesser Cross-Validated Accuracy Detection of Scans Without Evidence of Dopaminergic Deficit

Samay Jain, MD, June 2, 1974-September 8, 2016

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