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- W4313333265 abstract "Parkinson's disease (PD) is a multifactorial neurodegenerative disorder characterised by the progressive loss of dopaminergic neurons in the nigrostriatal tract. The identification of disease-modifying therapies is the Holy Grail of PD research, but to date no drug has been approved as such a therapy. A possible reason is the remarkable phenotypic heterogeneity of PD patients, which can generate confusion in the interpretation of results or even mask the efficacy of a therapeutic intervention. This heterogeneity should be taken into account in clinical trials, stratifying patients by their expected response to drugs designed to engage selected molecular targets. In this setting, stratification methods (clinical and genetic) should be supported by biochemical phenotyping of PD patients, in line with the deep phenotyping concept. Collection, from single patients, of a range of biological samples would streamline the generation of these profiles. Several studies have proposed biochemical characterisations of patient cohorts based on analysis of blood, cerebrospinal fluid, urine, stool, saliva and skin biopsy samples, with extracellular vesicles attracting increasing interest as a source of biomarkers. In this review we report and critically discuss major studies that used a biochemical approach to stratify their PD cohorts. The analyte most studied is α-synuclein, while other studies have focused on neurofilament light chain, lysosomal proteins, inflammasome-related proteins, LRRK2 and the urinary proteome. At present, stratification of PD patients, while promising, is still a nascent approach. Deep phenotyping of patients will allow clinical researchers to identify homogeneous subgroups for the investigation of tailored disease-modifying therapies, enhancing the chances of therapeutic success." @default.
- W4313333265 created "2023-01-06" @default.
- W4313333265 creator A5007019878 @default.
- W4313333265 creator A5059283344 @default.
- W4313333265 creator A5082836367 @default.
- W4313333265 creator A5010641170 @default.
- W4313333265 date "2023-02-01" @default.
- W4313333265 modified "2023-10-01" @default.
- W4313333265 title "Relevance of Biochemical Deep Phenotyping for a Personalised Approach to Parkinson’s Disease" @default.
- W4313333265 cites W110903188 @default.
- W4313333265 cites W1492555660 @default.
- W4313333265 cites W1839521047 @default.
- W4313333265 cites W1985458075 @default.
- W4313333265 cites W1997654801 @default.
- W4313333265 cites W2002405028 @default.
- W4313333265 cites W2007634950 @default.
- W4313333265 cites W2019738036 @default.
- W4313333265 cites W2025349319 @default.
- W4313333265 cites W2029085167 @default.
- W4313333265 cites W2032076557 @default.
- W4313333265 cites W2033596507 @default.
- W4313333265 cites W2070914201 @default.
- W4313333265 cites W2077047479 @default.
- W4313333265 cites W2093888310 @default.
- W4313333265 cites W2100837265 @default.
- W4313333265 cites W2116159247 @default.
- W4313333265 cites W2120753166 @default.
- W4313333265 cites W2140617063 @default.
- W4313333265 cites W2155815855 @default.
- W4313333265 cites W2232523475 @default.
- W4313333265 cites W2233667077 @default.
- W4313333265 cites W2252821559 @default.
- W4313333265 cites W2397760722 @default.
- W4313333265 cites W2419387266 @default.
- W4313333265 cites W2435254117 @default.
- W4313333265 cites W2563206242 @default.
- W4313333265 cites W2763160983 @default.
- W4313333265 cites W2789915296 @default.
- W4313333265 cites W2800283294 @default.
- W4313333265 cites W2806614570 @default.
- W4313333265 cites W2884714191 @default.
- W4313333265 cites W2889197740 @default.
- W4313333265 cites W2896618046 @default.
- W4313333265 cites W2899031631 @default.
- W4313333265 cites W2900756811 @default.
- W4313333265 cites W2900976680 @default.
- W4313333265 cites W2907961615 @default.
- W4313333265 cites W2909458901 @default.
- W4313333265 cites W2912689631 @default.
- W4313333265 cites W2914576615 @default.
- W4313333265 cites W2933304441 @default.
- W4313333265 cites W2946510780 @default.
- W4313333265 cites W2952265197 @default.
- W4313333265 cites W2954141541 @default.
- W4313333265 cites W2959967629 @default.
- W4313333265 cites W2972588477 @default.
- W4313333265 cites W2983163271 @default.
- W4313333265 cites W2990845518 @default.
- W4313333265 cites W3005252823 @default.
- W4313333265 cites W3005814496 @default.
- W4313333265 cites W3006954961 @default.
- W4313333265 cites W3011595648 @default.
- W4313333265 cites W3019725621 @default.
- W4313333265 cites W3026426657 @default.
- W4313333265 cites W3034760723 @default.
- W4313333265 cites W3036069297 @default.
- W4313333265 cites W3036560286 @default.
- W4313333265 cites W3041920463 @default.
- W4313333265 cites W3048206208 @default.
- W4313333265 cites W3084651803 @default.
- W4313333265 cites W3108701529 @default.
- W4313333265 cites W3111428628 @default.
- W4313333265 cites W3118252084 @default.
- W4313333265 cites W3118581546 @default.
- W4313333265 cites W3121728483 @default.
- W4313333265 cites W3126430521 @default.
- W4313333265 cites W3132630320 @default.
- W4313333265 cites W3132644187 @default.
- W4313333265 cites W3134641471 @default.
- W4313333265 cites W3135877609 @default.
- W4313333265 cites W3139030108 @default.
- W4313333265 cites W3163110391 @default.
- W4313333265 cites W3173176443 @default.
- W4313333265 cites W3174232220 @default.
- W4313333265 cites W3188699411 @default.
- W4313333265 cites W3192117379 @default.
- W4313333265 cites W3207425769 @default.
- W4313333265 cites W3209265564 @default.
- W4313333265 cites W4200084373 @default.
- W4313333265 cites W4211044101 @default.
- W4313333265 cites W4211151576 @default.
- W4313333265 cites W4289667121 @default.
- W4313333265 doi "https://doi.org/10.1016/j.neuroscience.2022.12.019" @default.
- W4313333265 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36572171" @default.
- W4313333265 hasPublicationYear "2023" @default.
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