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Biomarkers that predict conversion from isolated REM sleep behaviour disorder to Parkinson disease are urgently needed. A new study finds that detection of misfolded α-synuclein in the cerebrospinal fluid is a good marker of conversion risk, but an inability to predict the timeline of progression might limit its utility.

Parkinson’s disease is increasingly prevalent. It progresses from the pre-motor stage (characterised by non-motor symptoms like REM sleep behaviour disorder), to the disabling motor stage. We need objective biomarkers for early/pre-motor disease stages to be able to intervene and slow the underlying neurodegenerative process. Here, we validate a targeted multiplexed mass spectrometry assay for blood samples from recently diagnosed motor Parkinson’s patients ( n  = 99), pre-motor individuals with isolated REM sleep behaviour disorder (two cohorts: n  = 18 and n  = 54 longitudinally), and healthy controls ( n  = 36). Our machine-learning model accurately identifies all Parkinson patients and classifies 79% of the pre-motor individuals up to 7 years before motor onset by analysing the expression of eight proteins—Granulin precursor, Mannan-binding-lectin-serine-peptidase-2, Endoplasmatic-reticulum-chaperone-BiP, Prostaglaindin-H2-D-isomaerase, Interceullular-adhesion-molecule-1, Complement C3, Dickkopf-WNT-signalling pathway-inhibitor-3, and Plasma-protease-C1-inhibitor. Many of these biomarkers correlate with symptom severity. This specific blood panel indicates molecular events in early stages and could help identify at-risk participants for clinical trials aimed at slowing/preventing motor Parkinson’s disease. Parkinson’s disease is lacking easily accessible biomarkers. Here the authors show, that targeted blood proteomics is feasible to identify the patients and to predict the phenoconvertion in prodromal subjects up to 7 years before symptom onset.

Aggregation and spreading of α-Synuclein (αSyn) are hallmarks of several neurodegenerative diseases, thus monitoring human αSyn (hαSyn) in animal models or cell cultures is vital for the field. However, the detection of native hαSyn in such systems is challenging. We show that the nanobody NbSyn87, previously-described to bind hαSyn, also shows cross-reactivity for the proteasomal subunit Rpn10. As such, when the NbSyn87 is expressed in the absence of hαSyn, it is continuously degraded by the proteasome, while it is stabilized when it binds to hαSyn. Here, we exploit this feature to design a new Fluorescent Reporter for hαSyn (FluoReSyn) by fusing NbSyn87 to fluorescent proteins, which results in fluorescence signal fluctuations depending on the presence and amounts of intracellular hαSyn. We characterize this biosensor in cells and tissues to finally reveal the presence of transmittable αSyn in human cerebrospinal fluid, demonstrating the potential of FluoReSyn for clinical research and diagnostics. α-Syn in CSF is a biomarker of neurodegenerative diseases; however, the detection of clinically relevant species is difficult. Here, the authors create a nanobody biosensor that reveals the presence of α-Syn in cells, which allow the detection of transmittable forms of α-Syn present in human CSF.

With the advent of the genetic era in Parkinson’s disease (PD) research in 1997, α-synuclein was identified as an important player in a complex neurodegenerative disease that affects >10 million people worldwide. PD has been estimated to have an economic impact of $51.9 billion in the US alone. Since the initial association with PD, hundreds of researchers have contributed to elucidating the functions of α-synuclein in normal and pathological states, and these remain critical areas for continued research. With this position paper the authors strive to achieve two goals: first, to succinctly summarize the critical features that define α-synuclein’s varied roles, as they are known today; and second, to identify the most pressing knowledge gaps and delineate a multipronged strategy for future research with the goal of enabling therapies to stop or slow disease progression in PD.

Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy are brain disorders characterised by intracellular α-synuclein deposits. We aimed to assess whether reduction of α-synuclein concentrations in CSF was a marker for α-synuclein deposition in the brain, and therefore diagnostic of synucleinopathies. We assessed potential extracellular-fluid markers of α-synuclein deposition in the brain (total α-synuclein and total tau in CSF, and total α-synuclein in serum) in three cohorts: a cross-sectional training cohort of people with Parkinson's disease, multiple system atrophy, dementia with Lewy bodies, Alzheimer's disease, or other neurological disorders; a group of patients with autopsy-confirmed dementia with Lewy bodies, Alzheimer's disease, or other neurological disorders (CSF specimens were drawn ante mortem during clinical investigations); and a validation cohort of patients who between January, 2003, and December, 2006, were referred to a specialised movement disorder hospital for routine inpatient admission under the working diagnosis of parkinsonism. CSF and serum samples were assessed by ELISA, and clinical diagnoses were made according to internationally established criteria. Mean differences in biomarkers between diagnostic groups were assessed with conventional parametric and non-parametric statistics. In our training set, people with Parkinson's disease, multiple system atrophy, and dementia with Lewy bodies had lower CSF α-synuclein concentrations than patients with Alzheimer's disease and other neurological disorders. CSF α-synuclein and tau values separated participants with synucleinopathies well from those with other disorders (p<0·0001; area under the receiver operating characteristic curve [AUC]=0·908). In the autopsy-confirmed cases, CSF α-synuclein discriminated between dementia with Lewy bodies and Alzheimer's disease (p=0·0190; AUC=0·687); in the validation cohort, CSF α-synuclein discriminated Parkinson's disease and dementia with Lewy bodies versus progressive supranuclear palsy, normal-pressure hydrocephalus, and other neurological disorders (p<0·0001; AUC=0·711). Other predictor variables tested in this cohort included CSF tau (p=0·0798), serum α-synuclein (p=0·0502), and age (p=0·0335). CSF α-synuclein concentrations of 1·6 pg/μL or lower showed 70·72% sensitivity (95% CI 65·3–76·1%) and 52·83% specificity (39·4–66·3%) for the diagnosis of Parkinson's disease. At this cutoff, the positive predictive value for any synucleinopathy was 90·7% (95% CI 87·3–94·2%) and the negative predictive value was 20·4% (13·7–27·2%). Mean CSF α-synuclein concentrations as measured by ELISA are significantly lower in Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy than in other neurological diseases. Although specificity was low, the high positive predictive value of CSF α-synuclein concentrations in patients presenting with synucleinopathy-type parkinsonism might be useful in stratification of patients in future clinical trials. American Parkinson Disease Association, Stifterverband für die Deutsche Wissenschaft, Michael J Fox Foundation for Parkinson's Research, National Institutes of Health, Parkinson Research Consortium Ottawa, and the Government of Canada.

ObjectiveTo examine the baseline prevalence and longitudinal evolution in non-motor symptoms (NMS) in a prospective cohort of, at baseline, patients with de novo Parkinson’s disease (PD) compared with healthy controls (HC).MethodsParkinson’s Progression Markers Initiative (PPMI) is a longitudinal, ongoing, controlled study of de novo PD participants and HC. NMS were rated using the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part I score and other validated NMS scales at baseline and after 2 years. Biological variables included cerebrospinal fluid (CSF) markers and dopamine transporter imaging.Results423 PD subjects and 196 HC were enrolled and followed for 2 years. MDS-UPDRS Part I total mean (SD) scores increased from baseline 5.6 (4.1) to 7.7 (5.0) at year 2 in PD subjects (p<0.001) versus from 2.9 (3.0) to 3.2 (3.0) in HC (p=0.38), with a significant difference between the groups (p<0.001). In the multivariate analysis, higher baseline NMS score was associated with female sex (p=0.008), higher baseline MDS-UPDRS Part II scores (p<0.001) and more severe motor phenotype (p=0.007). Longitudinal increase in NMS severity was associated with the older age (0.008) and lower CSF Aβ1–42 (0.005) at baseline. There was no association with the dose or class of dopaminergic therapy.ConclusionsThis study of NMS in early PD identified clinical and biological variables associated with both baseline burden and predictors of progression. The association of a greater longitudinal increase in NMS with lower baseline Aβ1–42 level is an important finding that will have to be replicated in other cohorts.Trial registrationClinicalTrials.gov identifier: NCT01141023.

Several pathophysiological processes are involved in Parkinson's disease (PD) and could inform in vivo biomarkers. We assessed an established biomarker panel, validated in Alzheimer's Disease, in a PD cohort. Longitudinal cerebrospinal fluid (CSF) samples from PPMI (252 PD, 115 healthy controls, HC) were analyzed at six timepoints (baseline, 6, 12, 24, 36, and 48 months follow-up) using Elecsys® electrochemiluminescence immunoassays to quantify neurofilament light chain (NfL), soluble TREM2 receptor (sTREM2), chitinase-3-like protein 1 (YKL40), glial fibrillary acidic protein (GFAP), interleukin-6 (IL-6), S100, and total [alpha]-synuclein ([alpha]Syn). [alpha]Syn was significantly lower in PD (mean 103 pg/ml vs. HC: 127 pg/ml, p0.05) and none showed a significant difference longitudinally. We found significantly higher levels of all these markers between PD patients who developed cognitive decline during follow-up, except for [alpha]Syn and IL-6. Except for [alpha]Syn, the additional biomarkers did not differentiate PD and HC, and none showed longitudinal differences, but most markers predict cognitive decline in PD during follow-up.

Digital health technologies enable remote and therefore frequent measurement of motor signs, potentially providing reliable and valid estimates of motor sign severity and progression in Parkinson’s disease (PD). The Roche PD Mobile Application v2 was developed to measure bradykinesia, bradyphrenia and speech, tremor, gait and balance. It comprises 10 smartphone active tests (with ½ tests administered daily), as well as daily passive monitoring via a smartphone and smartwatch. It was studied in 316 early-stage PD participants who performed daily active tests at home then carried a smartphone and wore a smartwatch throughout the day for passive monitoring (study NCT03100149). Here, we report baseline data. Adherence was excellent (96.29%). All pre-specified sensor features exhibited good-to-excellent test–retest reliability (median intraclass correlation coefficient = 0.9), and correlated with corresponding Movement Disorder Society–Unified Parkinson's Disease Rating Scale items (rho: 0.12–0.71). These findings demonstrate the preliminary reliability and validity of remote at-home quantification of motor sign severity with the Roche PD Mobile Application v2 in individuals with early PD.

L1 cell adhesion molecule (L1CAM)-positive extracellular vesicles (EVs) are being explored as a potential source of biomarkers for Parkinson's disease (PD) in peripheral blood. However, their utility remains controversial. In this study, we sought to investigate the proteome composition of L1CAM -EVs isolated from human blood plasma and evaluate their potential as biomarkers for PD. L1CAM -EVs were extracted from blood plasma using direct immunoprecipitation by employing magnetic beads coupled to an anti-L1CAM antibody. The Proximity Extension Assay platform, Olink Explore 3072, was used to analyze samples from 60 individuals: 20 healthy controls (HC), 20 patients with isolated REM sleep behavior disorder (iRBD), and 20 PD patients. Targeted proteomic analysis identified 2841 proteins in L1CAM -EVs, of which 203 exhibited differential expression across groups. Although these changes were not statistically significant, after correction for multiple testing, a combination of 12 proteins could discriminate between PD and HC. Moreover, several proteins displayed trends toward upregulation or downregulation in PD and iRBD when compared with HC. These preliminary findings suggest that L1CAM -EVs proteins show some potential as biomarkers for PD; however, further investigation and validation studies are required.

Cerebrospinal fluid is investigated in biomarker studies for various neurological disorders of the central nervous system due to its proximity to the brain. Currently, only a limited number of biomarkers have been validated in independent studies. The high variability in the protein composition and protein abundance of cerebrospinal fluid between as well as within individuals might be an important reason for this phenomenon. To evaluate this possibility, we investigated the inter- and intraindividual variability in the cerebrospinal fluid proteome globally, with a specific focus on disease biomarkers described in the literature. Cerebrospinal fluid from a longitudinal study group including 12 healthy control subjects was analyzed by label-free quantification (LFQ) via LC-MS/MS. Data were quantified via MaxQuant. Then, the intra- and interindividual variability and the reference change value were calculated for every protein. We identified and quantified 791 proteins, and 216 of these proteins were abundant in all samples and were selected for further analysis. For these proteins, we found an interindividual coefficient of variation of up to 101.5% and an intraindividual coefficient of variation of up to 29.3%. Remarkably, these values were comparably high for both proteins that were published as disease biomarkers and other proteins. Our results support the hypothesis that natural variability greatly impacts cerebrospinal fluid protein biomarkers because high variability can lead to unreliable results. Thus, we suggest controlling the variability of each protein to distinguish between good and bad biomarker candidates, e.g., by utilizing reference change values to improve the process of evaluating potential biomarkers in future studies.