Objective: To analyze biomarker signatures in biofluids in prodromal Parkinson’s disease (PD) to identify subjects at-risk to conversion and for progression.

Background: Individuals at-risk for PD serve as a population for therapies targeted at preventing these disorders. The Parkinson Progression Marker Initiative (PPMI) is a longitudinal, observational, multi-center study with multimodal biomarkers to assess the natural history of prodromal and manifest PD and healthy controls (HC) to provide predictive models of progression.

Method: We studied individuals with isolated REM sleep behavior disorder (iRBD, n=39) or hyposmia (n=26) and dopamine transporter binding deficit. We analyzed several cerebrospinal fluid (CSF) and blood biomarkers and compared them to 415 de novo PD subjects and 196 HC at baseline (BL) and up to 4 timepoints longitudinally: total alpha-synuclein, total tau-protein, phosphorylated tau protein (p-tau 181) and beta-amyloid 1-42 in CSF as well as uric acid and neurofilament light chain (NfL) in serum.

Results: Results: At group level, compared to HC, the 65 prodromal subjects showed lower BL levels of CSF total alpha-synuclein (p=0.011), beta-amyloid 1-42 (p=0.016), similar to the de novo PD cohort. The prodromal group also featured higher BL levels of total and phosphorylated tau protein (p=0.003 and <0.001) and serum NfL (p<0.001). CSF total and phosphorylated tau protein and serum NfL increased longitudinally. Until 3/2021, 22 prodromal subjects developed PD (N=17) or other neurological disorders (OND; 3 DLB, 2 MSA). For serum NfL converters to OND showed higher values compared to converters to PD. CSF p-tau levels were significantly lower in those who later developed PD compared to HC and trend level correlations were observed between p-tau and cognitive deficits monitored by MoCA.

Conclusion: Several biofluid biomarkers are already altered in prodromal stage and can identify subjects at-risk to convert to disease. Ongoing studies on immune system, proteins and metabolites as well as machine learning techniques would provide multimodal signatures and predictive models for disease-modifying therapies. Additionally, the expansion of PPMI has a more strategic focus on recruitment of prodromal subjects (NCT04477785) to enhance the power of to identify biomarkers.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/biofluid-biomarkers-in-prodromal-parkinsons-disease/