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Published on: July 23, 2012
MicroRNAs in Parkinson's disease: a systematic review and diagnostic accuracy meta-analysis
Diane Guévremont1,2, Joyeeta Roy1,2,3, Nicholas J Cutfield2,3
1Department of Anatomy, University of Otago, Dunedin, New Zealand.
Abstract:
Current clinical tests for Parkinson's disease (PD) provide insufficient diagnostic accuracy leading to an urgent need for improved diagnostic biomarkers. As microRNAs (miRNAs) are promising biomarkers of various diseases, including PD, this systematic review and meta-analysis aimed to assess the diagnostic accuracy of biofluid miRNAs in PD. All studies reporting data on miRNAs expression in PD patients compared to controls were included. Gene targets and significant pathways associated with miRNAs expressed in more than 3 biofluid studies with the same direction of change were analyzed using target prediction and enrichment analysis. A bivariate model was used to calculate sensitivity, specificity, likelihood ratios, and diagnostic odds ratio. While miR-24-3p and miR-214-3p were the most reported miRNA (7 each), miR-331-5p was found to be consistently up regulated in 4 different biofluids. Importantly, miR-19b-3p, miR-24-3p, miR-146a-5p, and miR-221-3p were reported in multiple studies without conflicting directions of change in serum and bioinformatic analysis found the targets of these miRNAs to be associated with pathways important in PD pathology. Of the 102 studies from the systematic review, 15 studies reported sensitivity and specificity data on combinations of miRNAs and were pooled for meta-analysis. Studies (17) reporting sensitivity and specificity data on single microRNA were pooled in a separate meta-analysis. Meta-analysis of the combinations of miRNAs (15 studies) showed that biofluid miRNAs can discriminate between PD patients and controls with good diagnostic accuracy (sensitivity = 0.82, 95% CI 0.76-0.87; specificity = 0.80, 95% CI 0.74-0.84; AUC = 0.87, 95% CI 0.83-0.89). However, we found multiple studies included more males with PD than any other group therefore possibly introducing a sex-related selection bias. Overall, our study captures key miRNAs which may represent a point of focus for future studies and the development of diagnostic panels whilst also highlighting the importance of appropriate study design to develop representative biomarker panels for the diagnosis of PD.
Insights
MicroRNAs in biofluids show promise as diagnostic biomarkers for Parkinson's disease (PD), with potential for developing accurate diagnostic panels. Further research is needed to address potential biases and refine biomarker selection.
Area of Science:
- Biomarkers
- Neurodegenerative Diseases
- Molecular Diagnostics
Background:
- Current Parkinson's disease (PD) diagnostic methods lack sufficient accuracy, highlighting the need for novel biomarkers.
- MicroRNAs (miRNAs) are recognized as potential biomarkers for various diseases, including PD, due to their stability and detectability in biofluids.
Approach:
- A systematic review and meta-analysis were conducted to evaluate the diagnostic accuracy of biofluid miRNAs for PD.
- Studies reporting miRNA expression in PD patients versus controls were included, with gene targets and pathways analyzed.
- Bivariate models were used to calculate diagnostic accuracy metrics, including sensitivity, specificity, and AUC.
Key Points:
- miR-331-5p was consistently upregulated across four different biofluids.
- Specific miRNAs (miR-19b-3p, miR-24-3p, miR-146a-5p, miR-221-3p) showed consistent expression patterns in serum and their targets are linked to PD pathology.
- Meta-analysis of miRNA combinations demonstrated good diagnostic accuracy (AUC=0.87) for distinguishing PD patients from controls.
Conclusions:
- Biofluid miRNAs, particularly combinations, exhibit significant potential for accurate PD diagnosis.
- Identified key miRNAs warrant further investigation for developing diagnostic panels.
- Attention to study design is crucial to mitigate potential biases, such as sex-related selection bias, for representative biomarker development.
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