Clinical markers of disease progression in the prodromal to overt alpha-synucleinopathy continuum

Clicks: 2
ID: 315298
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #329 of 375 articles by views in Brain research

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 375 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Abstract Clinical progression from prodromal to overt stages of alpha-synucleinopathies is highly heterogeneous, and there is an urgent need for reliable clinical progression markers. Exploiting the Disease Course Map (DCM) model, we investigated how clinical signs evolve in patients with idiopathic/isolated rapid-eye-movement sleep behavior disorder (iRBD), extracting clinical progression measures for use at the single-subject level. Furthermore, we correlated them with both established and innovative neurodegeneration biomarkers. We trained a DCM model using cognitive and motor scores of a longitudinal cohort of 766 iRBD patients (166 female, 67.9 ± 7.4 years). We personalized the model by extracting three parameters to describe the single subject in comparison to the averaged population data. We tested the model on a blind set of 49 iRBD patients (7 female, 68.5 ± 7.1 years) who underwent both longitudinal clinical evaluations and instrumental evaluation at the first observation. In the blind set, we correlated the individual model parameters with presynaptic dopaminergic impairment, an established biomarker of substantia nigra neurodegeneration, and cortical electrophysiological dysfunction—measured by high-density electroencephalography (HD-EEG)—an innovative neurodegeneration biomarker. We identified three individual clinical markers reflecting early/late (time shift, τ) and fast/slow (acceleration factor, α) disease progression, as well as the individual clinical trajectory (i.e., earlier motor or cognitive impairment, intermarker spacing, ω). The individual model parameters are significantly associated with phenoconversion, with a 73% chance of distinguishing between clinically stable patients (non-converters) and those converting during the longitudinal observation to an overt alpha-synucleinopathy (converters). Motor scores progress 35% faster than cognitive scores in our iRBD cohort. Converter iRBD patients exhibited a faster and earlier disease progression than non-converters, and, on average, they showed an earlier worsening of motor scores than cognitive scores, regardless of the clinical diagnosis of overt parkinsonism. Patients with iRBD who developed parkinsonism worsened earlier than those who develop dementia. At baseline, an earlier progression was related to presynaptic dopaminergic impairment and higher phase synchronization in the theta band (4-8 Hz). Higher synchronization in the theta band was also associated with an earlier worsening of motor scores than cognitive scores. In this study, we investigated a large longitudinal iRBD cohort, applying an advanced disease progression model. We found three individual clinical markers that were able to monitor disease progression and showed significant association with both established and innovative neurodegeneration biomarkers. We suggest that these clinical markers could be used as efficacy endpoints in disease-modifying clinical trials.
Reference Key
openalex_W7162752883 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Monica Roascio, Elena Antelmi, Luca Baldelli, Francesco Biscarini, Enrica Bonanni, Valerio Brunetti, Elena Capriglia, Claudio Geromino, F. Casoni, Flavia Cirillo, Giacomo Della Marca, Felice Di Laudo, L Ferini-Strambi, M Sousa Fernandes, Michela Figorilli, Andrea Galbiati, Gian Luigi Gigli, Biancamaria Guarnieri, Domeniko Hoxhaj, Giuseppe Lanza, Claudio Liguori, Michelangelo Maestri Tassoni, Silvia Maio, Gaetano Malomo, Sara Marelli, Pietro Mattioli, Martina Mulas, Beatrice Orso, Matteo Pardini, G Plazzi, Federica Provini, Gaia Pellitteri, M. Puligheddu, Michele Terzaghi, Amélie Pelletier, Ronald B Postuma, Gabriele Arnulfo, Dario Arnaldi
Journal Brain research
Year 2026
DOI
10.1093/brain/awag193
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.