Can the prediction model using regression with optimal scale improve the power to predict the Parkinson's dementia?

BACKGROUND Efficiently detecting Parkinson's disease(PD)with dementia(PDD)as soon as possible is an important issue in geriatric medicine.AIM To develop a model for predicting PDD based on various neuropsychological tests using data from a nationwide survey conducted by the Korean Centers for Disease Control and Prevention and to present baseline data for the early detection of PDD.METHODS This study comprised 289 patients who were 60 years or older with PD[110 with PDD and 179 Parkinson's Disease-Mild Cognitive Impairment(PD-MCI)].Regression with optimal scaling(ROS)was used to identify independent relationships between the neuropsychological test results and PDD.RESULTS In the ROS analysis,Korean version of mini mental state ex-amination(MMSE)(KOREAN version of MMSE)(b=-0.52,SE=0.16)and Hoehn and Yahr staging(b=0.44,SE=0.19)were significantly effective models for distinguishing PDD from PD-MCI(P<0.05),even after adjusting for all of the Parkinson's motor symptom and neuropsychological test results.The optimal number of categories(scaling factors)for KOREAN version of MMSE and Hoehn and Yahr Scale was 10 and 7,respectively.CONCLUSION The results of this study suggest that among the various neuropsychological tests conducted,the optimal classification scores for KOREAN version of MMSE and Hoehn and Yahr Scale could be utilized as an effective screening test for the early discrimination of PDD from PD-MCI.

Several miRNAs derived from serum extracellular vesicles are potential biomarkers for early diagnosis and progression of Parkinson’s disease

Background:Blood-based test for predicting disease progression and early diagnosis of Parkinson’s disease(PD)is an unmet need in the clinic.The profiles of microRNAs(miRNAs)are regarded as potential diagnostic biomarkers for human diseases,whereas miRNAs in the periphery are susceptible to the influence of various components.MiRNAs enriched in serum extracellular vesicles(EVs)have demonstrated disease-specific advantages in diagnosis due to their high abundance,stability and resistance to degradation.This study was aimed to screen differentially expressed EV-derived miRNAs between healthy controls and PD patients to aid in diagnosis of PD.Methods:A total of 31 healthy controls and 72 patients with a diagnosis of PD at different Hoehn and Yahr stages in Tangdu Hospital were included.In total,185 differentially expressed miRNAs were obtained through RNA sequencing of serum EVs as well as edgeR and t-test analyses.Subsequently,the weighted gene co-expression network analysis(WGCNA)was utilized to identify the commonly expressed miRNAs in all stages of PD by constructing connections between modules,and specifically expressed miRNAs in each stage of PD by functional enrichment analysis.After aligning these miRNAs with PD-related miRNAs in Human miRNA Disease Database,the screened miRNAs were further validated by receiver operating characteristic(ROC)curves and quantitative real-time polymerase chain reaction(qRT-PCR)using peripheral blood EVs from 40 more participants.Results:WGCNA showed that 4 miRNAs were commonly associated with all stages of PD and 13 miRNAs were specifically associated with different stages of PD.Of the 17 obtained miRNAs,7 were validated by ROC curve analysis and 7 were verified in 40 more participants by qRT-PCR.Six miRNAs were verified by both methods,which included 2 miRNAs that were commonly expressed in all stages of PD and 4 miRNAs that were specifically expressed in different stages of PD.Conclusions:The 6 serum EV-derived miRNAs,hsa-miR-374a-5p,hsa-miR-374b-5p,hsa-miR-199a-3p,hsa-miR-28-5p,hsa-miR-22-5p and hsa-miR-151a-5p,may potentially be used as biomarkers for PD progression and for early diagnosis of PD in populations.