Abstract
Natural-history datasets have become pivotal for drug development and for shaping clinical-practice guidelines in rare diseases, yet many lysosomal storage disorders would benefit from deep phenotyping and modern analytic methods. Our objective was to integrate the past decade of genomic, cellular, treatment-outcome, and regulatory advances into a practical framework for capturing and interpreting natural-history data, using Gaucher disease (GD) as a paradigm. We reviewed more than 300 peer-reviewed articles (2005-2025), FDA guidance documents, and output from large real-world registries. Particular attention was paid to long-read GBA sequencing, biomarkers such as lyso-Gb1, emerging newborn screening programs, and preliminary observational work that points toward multi-state disease modeling. Key observations include: (i) Whole-gene sequencing has expanded genotype-phenotype maps, revealing more than 70 recombinant GBA alleles that confound panel tests; (ii) registry trajectories suggest that formal multi-state models could capture treatment-modified courses and silent endpoints-monoclonal gammopathy, malignancy, Parkinson's disease, pulmonary arterial hypertension-better than current summary statistics; (iii) lyso-Gb1 outperforms legacy biomarkers and now serves as a second-tier newborn-screening marker; (iv) Robust natural-history evidence has already underpinned regulatory approvals across several lysosomal disorders-including olipudase alfa for ASMD, cerliponase alfa for CLN2, vestronidase alfa for MPS VII, and sebelipase alfa for infantile-onset LAL-D-demonstrating that well-curated registries can serve as viable external controls for future LSD submissions. The convergence of deep phenotyping, genotype-aware analytics, and systematic biomarker capture promises to transform natural-history registries from descriptive archives into predictive engines. Gaucher disease offers a working template that, when extended across the LSD spectrum, can accelerate precision care and the development of next-generation therapeutics. Trial Registration: ClinicalTrials.gov identifier: NCT00358943, NCT03291223.