Parkinson disease (PD) affects up to 10 million people worldwide and is clinically diagnosed. Molecular phenotyping of patient samples might help to corroborate diagnosis, and a new study suggests that blood-based gene expression profiling might distinguish between patients with PD and those without. However, experience suggests that additional replication is needed.
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References
Adler, C. H. et al. Low clinical diagnostic accuracy of early versus advanced Parkinson disease: clinicopathologic study. Neurology 83, 406–412 (2014).
Shamir, R. et al. Analysis of blood-based gene expression in idiopathic Parkinson disease. Neurology 89, 1676–1683 (2017).
Scherzer, C. R. et al. Molecular markers of early Parkinson's disease based on gene expression in blood. Proc. Natl Acad. Sci. USA 104, 955–960 (2007).
Soreq, L., Israel, Z., Bergman, H. & Soreq, H. Advanced microarray analysis highlights modified neuro-immune signaling in nucleated blood cells from Parkinson's disease patients. J. Neuroimmunol. 201–202, 227–236 (2008).
Molochnikov, L. et al. A molecular signature in blood identifies early Parkinson's disease. Mol. Neurodegener. 7, 26 (2012).
Santiago, J. A. & Potashkin, J. A. Network-based metaanalysis identifies HNF4A and PTBP1 as longitudinally dynamic biomarkers for Parkinson's disease. Proc. Natl Acad. Sci. USA 112, 2257–2262 (2015).
Santiago, J. A. & Potashkin, J. A. Blood biomarkers associated with cognitive decline in early stage and drug-naive parkinson's disease patients. PLOS ONE 10, e0142582 (2015).
Cummings, J. L. et al. The role of dopaminergic imaging in patients with symptoms of dopaminergic system neurodegeneration. Brain 134, 3146–3166 (2011).
Marek, K. et al. The Parkinson Progression Marker Initiative (PPMI). Prog. Neurobiol. 95, 629–635 (2011).
Chen-Plotkin, A. S. Unbiased approaches to biomarker discovery in neurodegenerative diseases. Neuron 84, 594–607 (2014).
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Chen-Plotkin, A. Blood transcriptomics for Parkinson disease?. Nat Rev Neurol 14, 5–6 (2018). https://doi.org/10.1038/nrneurol.2017.166
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DOI: https://doi.org/10.1038/nrneurol.2017.166
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