ArticleJournal of Parkinson's disease2023
Nomogram for Early Prediction of Parkinson's Disease Based on microRNA Profiles and Clinical Variables.
Article in Journal of Parkinson's disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Development and validation a nomogram to predict long-term mortality risks of PRISm and mild-to-moderate COPD based on NHANES 2007-2012.Scientific reports · 2025Article
- Establishment and validation of a recurrence risk model in early-stage tongue squamous cell carcinoma patients incorporating immune-inflammatory biomarkers and clinicopathological parameters.American journal of cancer research · 2025Article
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Authors and funding
2 authors.
Funding
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Abstract
backgroundFew efficient and simple models for the early prediction of Parkinson's disease (PD) exists.
objectiveTo develop and validate a novel nomogram for early identification of PD by incorporating microRNA (miRNA) expression profiles and clinical indicators.
methodsExpression 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.
resultsAn 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.
conclusionThe constructed nomogram has potential for large-scale early screening of PD based upon its utility and precision.
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