A Novel Dynamic Graph Architecture for Staging Parkinson’s Disease Progression Using Cerebrospinal Fluids Longitudinal Profiles

Dynamic graph learning methods typically capture local structural information and short-range temporal dependencies at each time step. In this work, we introduce a dynamic graph learning architecture that generates time-step embeddings capturing both local structural context and progression-trajectory patterns for each node across an entire longitudinal sequence. The framework clusters fused embeddings that integrate (i) the global temporal trajectory of each node and (ii) its local spatial context at every graph snapshot to discover meaningful temporal patterns in longitudinal datasets. The proposed model was evaluated in the context of Parkinson’s disease (PD) progression using six years of longitudinal cerebrospinal fluid profiles from 24 patients. Visit-based graphs were constructed by representing patients as nodes enriched with peptide-abundance features, and by connecting patients with similar features profiles. A graph convolutional network captures visit-specific spatial relationships, while a sequential model learns global temporal representations. A fusion module integrates both sources of information to produce enriched node embeddings that reflect inter- and intra-patient molecular dynamics. Clustering of learned embeddings reveals four distinct stages of PD’s progression, supported by strong validity indices (Davies–Bouldin: 0.169; Calinski–Harabasz: 1264.24).Significant differences in motor severity (UPDRS 2 and UPDRS 3; p < 0.05) were observed in the groups, while the non-motor scores showed a more diffuse pattern (p = 0.11). Compared with existing features representation approaches, these findings demonstrate the potential of the proposed dynamic graph learning for data-driven disease staging and offer a generalizable framework to uncover latent temporal patterns in longitudinal datasets.