Dataset Information

SNCA Rep1 microsatellite length influences non-motor symptoms in early Parkinson's disease.

ABSTRACT: Long alpha-synuclein gene (SNCA) promoter (Rep1) allele-carriers are linked to higher risk for Parkinson's disease (PD) and faster motor progression. Non-motor symptoms including autonomic, neuropsychiatric, and sleep disorders are common in PD. However, the relationship between SNCA Rep1 microsatellite lengths and non-motor symptoms in early PD remains to be elucidated. 171 consecutive early PD patients were recruited from tertiary clinics and genotyped for Rep1. Multivariable regression analyses were performed to examine associations between Rep1 alleles and non-motor outcome scores. Longer Rep1 alleles significantly associated with higher total Non-Motor Symptom Scale (NMSS) scores (p=.006) and Hospital Anxiety and Depression Scale (HADS) depression subscale scores (p=.002), after adjusting for covariates and Bonferroni correction. We demonstrated that SNCA Rep1 allele length influences overall non-motor symptom burden and depression in early PD patients. Further functional studies to evaluate the role of Rep1 in non-dopaminergic systems may unravel new therapeutic targets for non-motor symptoms in PD.

SUBMITTER: Yong AC

PROVIDER: S-EPMC7655210 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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SNCA Rep1 microsatellite length influences non-motor symptoms in early Parkinson's disease.

Yong Alisa Cw AC Tan Yi Jayne YJ Zhao Yi Y Lu Zhonghao Z Ng Ebonne Yl EY Ng Samuel Ye SY Chia Nicole Sy NS Choi Xinyi X Heng Dede D Neo Shermyn S Xu Zheyu Z Tay Kay Yaw KY Au Wing Lok WL Tan Eng-King EK Tan Louis Cs LC Ng Adeline Sl AS

Aging 20201020 20

Long alpha-synuclein gene (SNCA) promoter (Rep1) allele-carriers are linked to higher risk for Parkinson's disease (PD) and faster motor progression. Non-motor symptoms including autonomic, neuropsychiatric, and sleep disorders are common in PD. However, the relationship between SNCA Rep1 microsatellite lengths and non-motor symptoms in early PD remains to be elucidated. 171 consecutive early PD patients were recruited from tertiary clinics and genotyped for Rep1. Multivariable reg ...[more]

PMID: 33082300

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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.

Dataset Information

SNCA Rep1 microsatellite length influences non-motor symptoms in early Parkinson's disease.

Publications

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

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