Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 3 de 3
Filtrar
Mais filtros

Base de dados
Ano de publicação
Tipo de documento
Intervalo de ano de publicação
1.
NPJ Parkinsons Dis ; 10(1): 102, 2024 May 17.
Artigo em Inglês | MEDLINE | ID: mdl-38760408

RESUMO

Lysosomal and synaptic dysfunctions are hallmarks in neurodegeneration and potentially relevant as biomarkers, but data on early Parkinson's disease (PD) is lacking. We performed targeted mass spectrometry with an established protein panel, assessing autophagy and synaptic function in cerebrospinal fluid (CSF) of drug-naïve de novo PD, and sex-/age-matched healthy controls (HC) cross-sectionally (88 PD, 46 HC) and longitudinally (104 PD, 58 HC) over 10 years. Multiple markers of autophagy, synaptic plasticity, and secretory pathways were reduced in PD. We added samples from prodromal subjects (9 cross-sectional, 12 longitudinal) with isolated REM sleep behavior disorder, revealing secretogranin-2 already decreased compared to controls. Machine learning identified neuronal pentraxin receptor and neurosecretory protein VGF as most relevant for discriminating between groups. CSF levels of LAMP2, neuronal pentraxins, and syntaxins in PD correlated with clinical progression, showing predictive potential for motor- and non-motor symptoms as a valid basis for future drug trials.

2.
Nat Commun ; 15(1): 4759, 2024 Jun 18.
Artigo em Inglês | MEDLINE | ID: mdl-38890280

RESUMO

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.


Assuntos
Biomarcadores , Doença de Parkinson , Proteômica , Humanos , Doença de Parkinson/sangue , Doença de Parkinson/diagnóstico , Biomarcadores/sangue , Masculino , Proteômica/métodos , Feminino , Idoso , Pessoa de Meia-Idade , Aprendizado de Máquina , Transtorno do Comportamento do Sono REM/sangue , Transtorno do Comportamento do Sono REM/diagnóstico , Estudos de Casos e Controles , Espectrometria de Massas
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA