TRACE: Trajectory Reconstruction and Clustering From Sparse Evidence For Disease Subtyping and Staging

Chronic progressive diseases often follow heterogeneous courses, with individuals differing in the pattern and timing of biomarker abnormalities. However, observed data are typically sparse and predominantly cross-sectional, with limited follow-up covering only short segments of disease progression. Reconstructing heterogeneous trajectories from such data requires disentangling variation along disease trajectories from subtype heterogeneity while inferring individuals' unknown subtype assignments and disease stages. Existing approaches may confound disease stage with subtype, scale poorly as the number of biomarkers increases, or fail to effectively integrate cross-sectional and longitudinal observations. We propose TRACE: Trajectory Reconstruction And Clustering from Sparse Evidence, a probabilistic framework that jointly reconstructs biomarker trajectories and infers disease subtypes and continuous stages. TRACE combines monotonic neural networks for flexible modeling and data-driven clustering of subtype-specific progression trajectories with temporal regularization derived from longitudinal observations when available. All model components are jointly optimized end to end using stochastic gradient descent, enabling efficient and scalable estimation. We evaluate TRACE on semi-simulated MRI biomarker data with known subtype and stage structures and demonstrate its ability to identify clinically meaningful subtypes and disease stages in real-world neurodegenerative disease datasets.