Nomogram for Early Prediction of Parkinson's Disease Based on microRNA Profiles and Clinical Variables.
J Parkinsons Dis
; 13(4): 473-484, 2023.
Article
em En
| MEDLINE
| ID: mdl-37212072
ABSTRACT
BACKGROUND:
Few efficient and simple models for the early prediction of Parkinson's disease (PD) exists.OBJECTIVE:
To develop and validate a novel nomogram for early identification of PD by incorporating microRNA (miRNA) expression profiles and clinical indicators.METHODS:
Expression levels of blood-based miRNAs and clinical variables from 1,284 individuals were downloaded from the Parkinson's Progression Marker Initiative database on June 1, 2022. Initially, the generalized estimating equation was used to screen candidate biomarkers of PD progression in the discovery phase. Then, the elastic net model was utilized for variable selection and a logistics regression model was constructed to establish a nomogram. Additionally, the receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves were utilized to evaluate the performance of the nomogram.RESULTS:
An accurate and externally validated nomogram was constructed for predicting prodromal and early PD. The nomogram is easy to utilize in a clinical setting since it consists of age, gender, education level, and transcriptional score (calculated by 10 miRNA profiles). Compared with the independent clinical model or 10 miRNA panel separately, the nomogram was reliable and satisfactory because the area under the ROC curve achieved 0.72 (95% confidence interval, 0.68-0.77) and obtained a superior clinical net benefit in DCA based on external datasets. Moreover, calibration curves also revealed its excellent prediction power.CONCLUSION:
The constructed nomogram has potential for large-scale early screening of PD based upon its utility and precision.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doença de Parkinson
/
MicroRNAs
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
J Parkinsons Dis
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China