&lt;i&gt;SNCA&lt;/i&gt; Rep1 microsatellite length influences non-motor symptoms in early Parkinson&amp;#x2019;s disease

Yong, Alisa Cw; Tan, Yi Jayne; Zhao, Yongxiang; Lu, Zejun; Ng, Ebonne Yl; Ng, Samuel Yong‐Ern; Chia, Nicole Shuang Yu; Choi, Xinyi; Heng, Dede Liana; Neo, Shermyn; Xu, Zheyu; Tay, Kay Yaw; Au, Wing Lok; Tan, Eng‐King; Tan, Louis; Ng, A.

doi:10.18632/aging.104111

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…In a clinical patients based study it was reported that, long REP1 alleles are associated with motor and non-motor functions in PD (Ng et al, 2019). Adding to this study, the role of SNCA Rep1 allele length in non-motor functions as well as depression in the early PD patients was also reported (Yong et al, 2020). It was argued that Markopoulou et al (2014) collected the data through telephonic interviews and hence there could be possibility of miscommunication.…”

Section: Inhibition Of Uptake Of α-Synuclein By Neighboring Cellsmentioning

confidence: 81%

“Janus-Faced” α-Synuclein: Role in Parkinson’s Disease

Ray

Mahalakshmi

Tuladhar

et al. 2021

Front. Cell Dev. Biol.

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Parkinson’s disease (PD) is a pathological condition characterized by the aggregation and the resultant presence of intraneuronal inclusions termed Lewy bodies (LBs) and Lewy neurites which are mainly composed of fibrillar α-synuclein (α-syn) protein. Pathogenic aggregation of α-syn is identified as the major cause of LBs deposition. Several mutations in α-syn showing varied aggregation kinetics in comparison to the wild type (WT) α-syn are reported in PD (A30P, E46K, H 50Q, G51D, A53E, and A53T). Also, the cell-to-cell spread of pathological α-syn plays a significant role in PD development. Interestingly, it has also been suggested that the pathology of PD may begin in the gastrointestinal tract and spread via the vagus nerve (VN) to brain proposing the gut–brain axis of α-syn pathology in PD. Despite multiple efforts, the behavior and functions of this protein in normal and pathological states (specifically in PD) is far from understood. Furthermore, the etiological factors responsible for triggering aggregation of this protein remain elusive. This review is an attempt to collate and present latest information on α-syn in relation to its structure, biochemistry and biophysics of aggregation in PD. Current advances in therapeutic efforts toward clearing the pathogenic α-syn via autophagy/lysosomal flux are also reviewed and reported.

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Section: Inhibition Of Uptake Of α-Synuclein By Neighboring Cellsmentioning

confidence: 81%

“Janus-Faced” α-Synuclein: Role in Parkinson’s Disease

Ray

Mahalakshmi

Tuladhar

et al. 2021

Front. Cell Dev. Biol.

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“…In a large cohort of 1134 Chinese PD patients, the prevalence of depression, assessed with the Hamilton Rating Scale for Depression (HADS), was significantly lower in carriers homozygous for the (CA)12 allele of the copy number variation of the SNCA Rep1 allele [ 46 ]. Results were later confirmed in a group of 171 patients from Singapore, where Rep1 longer allele (263 bp) carriers had a higher burden of non-motor symptoms investigated with the total score on the Non-Motor Symptoms Scale (NMSS) [ 47 ]. Furthermore, the authors showed that 263 bp allele carriers had a higher frequency of depression and fatigue, according to the Fatigue Severity Score [ 47 ].…”

Section: Genetic Variation Of Snca and Non-motor Featuresmentioning

confidence: 98%

“…Results were later confirmed in a group of 171 patients from Singapore, where Rep1 longer allele (263 bp) carriers had a higher burden of non-motor symptoms investigated with the total score on the Non-Motor Symptoms Scale (NMSS) [ 47 ]. Furthermore, the authors showed that 263 bp allele carriers had a higher frequency of depression and fatigue, according to the Fatigue Severity Score [ 47 ].…”

Section: Genetic Variation Of Snca and Non-motor Featuresmentioning

confidence: 98%

The Impact of SNCA Variations and Its Product Alpha-Synuclein on Non-Motor Features of Parkinson’s Disease

Magistrelli

Contaldi

Comi

2021

Life

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Parkinson’s disease (PD) is a common and progressive neurodegenerative disease, caused by the loss of dopaminergic neurons in the substantia nigra pars compacta in the midbrain, which is clinically characterized by a constellation of motor and non-motor manifestations. The latter include hyposmia, constipation, depression, pain and, in later stages, cognitive decline and dysautonomia. The main pathological features of PD are neuronal loss and consequent accumulation of Lewy bodies (LB) in the surviving neurons. Alpha-synuclein (α-syn) is the main component of LB, and α-syn aggregation and accumulation perpetuate neuronal degeneration. Mutations in the α-syn gene (SNCA) were the first genetic cause of PD to be identified. Generally, patients carrying SNCA mutations present early-onset parkinsonism with severe and early non-motor symptoms, including cognitive decline. Several SNCA polymorphisms were also identified, and some of them showed association with non-motor manifestations. The functional role of these polymorphisms is only partially understood. In this review we explore the contribution of SNCA and its product, α-syn, in predisposing to the non-motor manifestations of PD.

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Machine Learning for Early Detection of Cognitive Decline in Parkinson’s Disease Using Multimodal Biomarker and Clinical Data

Mohammadi,

Ng,

Tan

et al. 2024

Biomedicines

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Background: Parkinson’s disease (PD) is the second most common neurodegenerative disease, primarily affecting the middle-aged to elderly population. Among its nonmotor symptoms, cognitive decline (CD) is a precursor to dementia and represents a critical target for early risk assessment and diagnosis. Accurate CD prediction is crucial for timely intervention and tailored management of at-risk patients. This study used machine learning (ML) techniques to predict the CD risk over five-year in early-stage PD. Methods: Data from the Early Parkinson’s Disease Longitudinal Singapore (2014 to 2018) was used to predict CD defined as a one-unit annual decrease or a one-unit decline in Montreal Cognitive Assessment over two consecutive years. Four ML methods—AutoScore, Random Forest, K-Nearest Neighbors and Neural Network—were applied using baseline demographics, clinical assessments and blood biomarkers. Results: Variable selection identified key predictors of CD, including education year, diastolic lying blood pressure, diastolic standing blood pressure, systolic lying blood pressure, Hoehn and Yahr scale, body mass index, phosphorylated tau at threonine 181, total tau, Neurofilament light chain and suppression of tumorigenicity 2. Random Forest was the most effective, achieving an AUC of 0.93 (95% CI: 0.89, 0.97), using 10-fold cross-validation. Conclusions: Here, we demonstrate that ML-based models can identify early-stage PD patients at high risk for CD, supporting targeted interventions and improved PD management.

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<i>SNCA</i> Rep1 microsatellite length influences non-motor symptoms in early Parkinson’s disease

Cited by 3 publications

References 35 publications

“Janus-Faced” α-Synuclein: Role in Parkinson’s Disease

“Janus-Faced” α-Synuclein: Role in Parkinson’s Disease

The Impact of SNCA Variations and Its Product Alpha-Synuclein on Non-Motor Features of Parkinson’s Disease

Machine Learning for Early Detection of Cognitive Decline in Parkinson’s Disease Using Multimodal Biomarker and Clinical Data

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