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Identifying Parkinson's disease subtypes with motor and non-motor symptoms via model-based multi-partition clustering.

ABSTRACT: Identification of Parkinson's disease subtypes may help understand underlying disease mechanisms and provide personalized management. Although clustering methods have been previously used for subtyping, they have reported generic subtypes of limited relevance in real life practice because patients do not always fit into a single category. The aim of this study was to identify new subtypes assuming that patients could be grouped differently according to certain sets of related symptoms. To this purpose, a novel model-based multi-partition clustering method was applied on data from an international, multi-center, cross-sectional study of 402 Parkinson's disease patients. Both motor and non-motor symptoms were considered. As a result, eight sets of related symptoms were identified. Each of them provided a different way to group patients: impulse control issues, overall non-motor symptoms, presence of dyskinesias and pyschosis, fatigue, axial symptoms and motor fluctuations, autonomic dysfunction, depression, and excessive sweating. Each of these groups could be seen as a subtype of the disease. Significant differences between subtypes (P< 0.01) were found in sex, age, age of onset, disease duration, Hoehn & Yahr stage, and treatment. Independent confirmation of these results could have implications for the clinical management of Parkinson's disease patients.

SUBMITTER: Rodriguez-Sanchez F

PROVIDER: S-EPMC8654994 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Identifying Parkinson's disease subtypes with motor and non-motor symptoms via model-based multi-partition clustering.

Rodriguez-Sanchez Fernando F Rodriguez-Blazquez Carmen C Bielza Concha C Larrañaga Pedro P Weintraub Daniel D Martinez-Martin Pablo P Rizos Alexandra A Schrag Anette A Chaudhuri K Ray KR

Scientific reports 20211208 1

Identification of Parkinson's disease subtypes may help understand underlying disease mechanisms and provide personalized management. Although clustering methods have been previously used for subtyping, they have reported generic subtypes of limited relevance in real life practice because patients do not always fit into a single category. The aim of this study was to identify new subtypes assuming that patients could be grouped differently according to certain sets of related symptoms. To this p ...[more]

PMID: 34880345

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Project description:BackgroundIdentifying 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.MethodsWe 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.ResultsFor 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.ConclusionThe 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.

Project description:IntroductionParkinson's disease (PD) heterogeneity poses challenges to the current development of discovering the best therapeutic targets.MethodsHere, we employ K-means and hierarchical clustering algorithms on data from the Parkinson's Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. The features were specifically the gray matter volume and dopaminergic features of the neostriatum, i.e., the caudate, putamen, and anterior putamen. We use machine learning (ML) algorithms, including Random Forest, Logistic Regression, and Support Vector Machine, to evaluate the diagnostic power of the brain features and network patterns in differentiating the PD subtypes and distinguishing PD from HC. Finally, we assessed whether PD subtypes described through network-specific patterns are dependent on the APOE genotype.ResultsUsing data from 2396 subjects, we show that PD (n=2037) is highly associated with APOE ϵ2/ϵ4. Our findings reveal a significant DAT deficit in the left and right structures of the caudate, putamen, and anterior putamen in subjects with PD compared to subjects with SWEDD(n=137) or HC(n=222), and that APOE ϵ2/ϵ4 may accelerate DAT deficits and brain alterations in both PD and SWEDD. Furthermore, clinical symptoms of PD in subjects (SWEDD), which hardly validated by DAT scan data, can be explained by variations in APOE genotypes and other brain features beyond DAT. We show the existence of three networks states for the whole data, with the first network state describing the subjects in HC, while the remaining two network states describing the two PD subtypes-one network state typified by a mildly sparsely connected network (patterns) and the other network state characterized by a more intensified sparsity in their network. We also show that the two subtypes of PD are characterized by distinctly different levels of total gray matter volume and DAT deficit. ML models show that features extracted from brain structure and network patterns can serve as reliable biomarkers for PD and its subtypes, with the highest performance (100% AUC, 99.3% accuracy, 0.993 F1) demonstrated by the fine-tuned SVM model.ConclusionOur findings suggest that, while PD is generally associated with a larger DAT deficit in specific brain structures of the neostriatum, it exhibits intrinsic heterogeneity across individuals, which may stem from genetic factors. Such heterogeneity can be characterized by ML models and optimally mapped into network states, providing new insights to consider when developing personalized drugs.

Project description:Clinical symptoms of Parkinson's disease (PD) are classified into motor and non-motor symptoms. Mental disorders, especially depression, are one of the major non-motor manifestations of PD. However, the underlying mechanisms remain poorly understood. In the present study, 21 neurotransmitters associated with mental disorders were measured in serum samples from patients and controls using the ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) assay. Additionally, five clinical scales-the MDS Unified Parkinson's Disease Rating Scale (UPDRS), the Non-Motor Symptoms Scale (NMSS), the Mini-Mental State Examination (MMSE), the Hamilton Anxiety Scale (HAMA), and the Hamilton Depression Scale (HAMD)-were used to evaluate the severity of both motor and non-motor symptoms in PD patients. Analysis of neurotransmitter metabolism revealed significant changes in the tryptophan (Trp) metabolic pathway in PD patients. Specifically, levels of Trp, kynurenine (KYN), kynurenic acid (KA), nicotinamide (NAM), and 5-methoxyltryptamine (MeOTA) were substantially decreased. Additionally, three other excitation/inhibiting amino acids-glutamic acid (Glu), 4-aminobutyric acid (GABA), and aspartic acid (Asp)-also declined. Moreover, neurotransmitter conversion ratios, such as KA/KYN, nicotinamide/niacin (NAM/NA), 5-hydroxytryptophan/tryptophan (5-HTP/Trp), and quinolinic acid/kynurenic acid (QA/KA), provided more dynamic insights into disrupted neurotransmitter metabolism. Correlation analyses between scale scores and neurotransmitter levels showed that concentrations of xanthurenic acid (XA) and the turnover rate of 3-hydroxykynurenine (3-HK) were negatively correlated with UPDRS scores, while 5-hydroxytryptamine (5-HT) and GABA levels were negatively correlated with non-motor symptoms in PD patients. In summary, this study elucidates, for the first time, the potential association and dynamics between altered neurotransmitter metabolism and the etiology of PD in terms of motor and non-motor functions. These findings offer novel biomarkers and therapeutic targets for the treatment of PD.

Dataset Information

Identifying Parkinson's disease subtypes with motor and non-motor symptoms via model-based multi-partition clustering.

Publications

Identifying Parkinson's disease subtypes with motor and non-motor symptoms via model-based multi-partition clustering.

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