Possible predictors of phenoconversion in isolated REM sleep behaviour disorder: a systematic review and meta-analysis

Wang, Chunyi; Chen, Fangzheng; Li, Yuanyuan; Liu, Jun

doi:10.1136/jnnp-2021-328062

Cited by 13 publications

(13 citation statements)

References 37 publications

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“…Detected by various imaging modalities [19], it is present in all individuals with PD [2] and most individuals with DLB [20]. Dopaminergic dysfunction/degeneration begins years before onset of clinically-manifest disease (i.e., parkinsonism), but heralds its onset [8, 21,22]. Based on the current state of the field, the D dimension is categorical with two strata (D-or D+).…”

Section: Midbrain Dopaminergic Neuron Degeneration Ismentioning

confidence: 99%

Proposal for a Biologic Staging System of Parkinson’s Disease

Chahine

Merchant

Siderowf

et al. 2023

JPD

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The Parkinson’s disease (PD) research field has seen the advent of several promising biomarkers and a deeper understanding of the clinical features of the disease from the earliest stages of pathology to manifest disease. Despite progress, a biologically based PD staging system does not exist. Such staging would be a useful framework within which to model the disease, develop and validate biomarkers, guide therapeutic development, and inform clinical trials design. We propose that the presence of aggregated neuronal α-synuclein, dopaminergic neuron dysfunction/degeneration, and clinical signs and symptoms identifies a group of individuals that have Lewy body pathology, which in early stages manifests with what is now referred to as prodromal non-motor features and later stages with the manifestations of PD and related Lewy body diseases as defined by clinical diagnostic criteria. Based on the state of the field, we herein propose a definition and staging of PD based on biology. We present the biologic basis for such a staging system and review key assumptions and evidence that support the proposed approach. We identify gaps in knowledge and delineate crucial research priorities that will inform the ultimate integrated biologic staging system for PD.

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Section: Midbrain Dopaminergic Neuron Degeneration Ismentioning

confidence: 99%

Proposal for a Biologic Staging System of Parkinson’s Disease

Chahine

Merchant

Siderowf

et al. 2023

JPD

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“…This suggests that iRBD is consistently influenced by an underlying neurodegenerative process, which is also supported by findings of widespread Lewy body pathology and α-synuclein in iRBD individuals [ 120 ]. By pooling studies on biomarkers of iRBD–PD association, a recent meta-analysis showed that motor dysfunction, constipation, orthostatic hypotension, hyposmia, mild cognitive impairment, and abnormal color vision in iRBD are significantly associated with subsequent PD risk [ 121 ]. Other biomarkers on imaging, polysomnography (PSG) or EEG can also be used to monitor the neurodegenerative process in iRBD [ 122 ].…”

Section: Bi-directional Relationship Of Circadian Rhythm Dysregulatio...mentioning

confidence: 99%

Circadian disruption and sleep disorders in neurodegeneration

Shen

Xie

et al. 2023

Transl Neurodegener

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Disruptions of circadian rhythms and sleep cycles are common among neurodegenerative diseases and can occur at multiple levels. Accumulating evidence reveals a bidirectional relationship between disruptions of circadian rhythms and sleep cycles and neurodegenerative diseases. Circadian disruption and sleep disorders aggravate neurodegeneration and neurodegenerative diseases can in turn disrupt circadian rhythms and sleep. Importantly, circadian disruption and various sleep disorders can increase the risk of neurodegenerative diseases. Thus, harnessing the circadian biology findings from preclinical and translational research in neurodegenerative diseases is of importance for reducing risk of neurodegeneration and improving symptoms and quality of life of individuals with neurodegenerative disorders via approaches that normalize circadian in the context of precision medicine. In this review, we discuss the implications of circadian disruption and sleep disorders in neurodegenerative diseases by summarizing evidence from both human and animal studies, focusing on the bidirectional links of sleep and circadian rhythms with prevalent forms of neurodegeneration. These findings provide valuable insights into the pathogenesis of neurodegenerative diseases and suggest a promising role of circadian-based interventions.

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“…4 Age, olfactory function, cognitive function and motor function have been reported as clinical biomarkers to predict phenoconversion with a hazard ratio up to 3.16. 5,6 Neuroimaging of dopamine transporters provides a promising biomarker to predict phenoconversion; 7 however, it is expensive and has limited accessibility. Electroencephalography (EEG) is a safe and easy method to objectively measure brain activity.…”

Section: Introductionmentioning

confidence: 99%

EEG-based Machine Learning Models for the Prediction of Phenoconversion Time and Subtype in iRBD

Jeong,

Shin,

Byun

et al. 2023

Preprint

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Background: Idiopathic/Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of alpha-synucleinopathies and eventually phenoconverts to overt neurodegenerative diseases including Parkinson's disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy (MSA). Associations of baseline resting-state electroencephalography (EEG) with phenoconversion have been reported. Objectives: In this study, we aimed to develop machine learning models to predict phenoconversion time and subtype using baseline EEG features in patients with iRBD. Methods: At baseline, resting-state EEG and neurological assessments were performed on patients with iRBD. Calculated EEG features included spectral power, weighted phase lag index and Shannon entropy. Three models were used for survival prediction, and four models were used for alpha-synucleinopathy subtype prediction. The models were externally validated using data from a different institution. Results: A total of 236 iRBD patients were followed-up for up to eight years (mean 3.5 years), and 31 patients converted to alpha-synucleinopathies (16 PD, 9 DLB, 6 MSA). The best model for survival prediction was the random survival forest model with an integrated Brier score of 0.114 and a concordance index of 0.775. The K-nearest neighbor model was the best model for subtype prediction with an area under the receiver operating characteristic curve of 0.901. EEG slowing was an important feature for both models. Conclusions: Machine learning models using baseline EEG features can be used to predict phenoconversion time and its subtype in patients with iRBD. Further research including large sample data from many countries is needed to make a more robust model.

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Possible predictors of phenoconversion in isolated REM sleep behaviour disorder: a systematic review and meta-analysis

Cited by 13 publications

References 37 publications

Proposal for a Biologic Staging System of Parkinson’s Disease

Proposal for a Biologic Staging System of Parkinson’s Disease

Circadian disruption and sleep disorders in neurodegeneration

EEG-based Machine Learning Models for the Prediction of Phenoconversion Time and Subtype in iRBD

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