疾病进程映射模型识别前驱期至显性 α-突触核蛋白病的临床进展标志物

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 Mapping (DCM) model, we investigated how clinical signs evolve in patients with idiopathic/isolated REM sleep behaviour 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 patients with iRBD (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 patients with iRBD (seven 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 progress

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