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Proteomics and machine learning identify a distinct blood biomarker panel to detect Parkinson’s disease up to 7 years before motor disease

ABSTRACT: Parkinson’s disease (PD) is increasing in prevalence, yet we lack readily available and non-invasive diagnostic biomarkers. An unbiased discovery proteomics study in plasma from PD patients and healthy controls (HC, phase 0) showed increased inflammation and decreased Wnt-signalling. To validate these findings, a targeted and multiplexed assay was developed and applied to a phase I cohort consisting of 99 de novo PD, 36 HC, and 18 subjects with polysomnography-confirmed isolated REM-sleep behaviour disorder (iRBD). In phase II, the multiplexed panel was refined and applied to a second cohort, focusing on the earliest stage of the disease by analysing 54 individuals with confirmed iRBD including longitudinal follow-up samples. A machine learning model, based on the expression of eight proteins, correctly identified all PD patients (phase I) and classified 79% of the iRBD as PD (phase II) up to 7 years before phenoconversion. Several of the identified biomarkers correlated with motor and non-motor symptom severity. This specific blood pattern was already evident in most iRBD cases, indicating pre-motor molecular events. This allows for the early identification of subjects at risk for PD in a blood test years before motor disease, which could support stratifying participants in future clinical trials aimed at preventing PD.

ORGANISM(S): Homo Sapiens

SUBMITTER: Jenny Hallqvist

PROVIDER: PXD041419 | panorama | Tue Mar 12 00:00:00 GMT 2024

REPOSITORIES: PanoramaPublic

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Project description:Sub-thalamic deep brain stimulation (DBS) reversibly modulates ParkinsonM-bM-^@M-^Ys disease (PD) motor symptoms, providing an unusual opportunity to compare leukocyte transcripts in the same subjects before and after neurosurgery and after disconnecting the stimulus (ON-and OFF-stimulus). Here, we report rapid stimulus-induced and largely reversible changes in PD leukocyte transcripts, which were larger in scope than the disease-induced changes. These transcript changes classified advanced pre- from post-surgery PD patients and discriminated patients from controls. Moreover, the extent of changes correlated with the neurological efficacy of the DBS neurosurgery, and covered both regulatory pathways and individual transcript changes, e.g. SNCA, PARK7 and the splicing factor SFRS1. Following 1 hour OFF-stimulus, these changes were largely reversed. We extracted from these differences a modified transcripts signature which discriminated controls from advanced PD patients, pre- from post-surgery and ON-from OFF-stimulus conditions. A further gene-list independent analysis detected reversed pathways. Our findings suggest future uses of this approach and the discovered molecular signature for early diagnostics of PD and for identifying novel targets for therapeutic intervention in this and other DBS-treatable neurological diseases. 27 Total samples were analyzed. Study design included four conditions. PD patients (n=7) leukocyte blood samples were examined at 3 time points, and healthy control subjects (n=6) were examined once each. Samples were taken 1 day prior to sub-thalamic nucleous (STN) DBS treatment, several months after STN-DBS treatment upon optimal stimulation and following one hour electrical stimulation cessation. The off stimulation caused recruitment of the disease symptoms as measured by the Unified Parkinson's Disease Rating Scale motor section (UPDRS-III). The different stages are designated: S1 (pre-treatment), S2 (post STN-DBS) and S3 (upon off stimulation). All patients were on dopamine replacement therapy (DRT) when examined pre- and post-DBS off stimulation (albeit with reduced therapy dose post-DBS). To reduce biological variability among the samples which is not related to the study, only male subjects were included in this study. Samples from six age- and gender- matched healthy control subjects served to detect disease modified transcripts and for pre- and post- treatment comparisons. The study was approved by the human review board at the Hadassah University Hospital, Ein-Kerem (no. 6-07.09.07) in accordance with the Declaration of Helsinki. All study participants signed informed consent.

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Proteomics and machine learning identify a distinct blood biomarker panel to detect Parkinson’s disease up to 7 years before motor disease

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