Multi-omic enriched blood-derived digital signatures reveal mechanistic and confounding disease clusters for differential diagnosis.

Introduction: Understanding disease relationships through blood biomarkers offers a pathway toward data-driven taxonomy and precision medicine. Methods: In this study, we constructed a digital blood twin --a computational model derived from 103 disease signatures comprising longitudinal hematological and biochemical analytes. Profiles were standardized into a unified disease -analyte matrix, and pairwise Pearson correlations were computed to assess similarity across conditions. Results: Hierarchical clustering within a phylogenetic framework revealed consistent, robust grouping of hematopoietic disorders, while metabolic, endocrine, and respiratory diseases were more heterogeneous, reflecting weaker internal cohesion. To evaluate cluster structure, the tree was partitioned at a stringent distance threshold, yielding 16 groups. Enrichment analysis of the largest and most heterogeneous cluster (Cluster 9) demonstrated convergence on cytokine-signaling pathways, indicating shared immunological and inflammatory mechanisms that transcend conventional clinical boundaries. Dimensionality reduction using Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) corroborated the correlation-based results, consistently separating hematological diseases as a distinct cluster. Random Forest feature selection identified neutrophils, mean corpuscular volume (MCV), red blood cell count, and platelet count as the most discriminative analytes, reinforcing the role of hematopoietic markers as key drivers of disease stratification. Discussion: Collectively, these findings demonstrate that blood-derived digital signatures can recover clinically meaningful disease clusters while uncovering mechanistic overlaps across categories. The strong coherence of hematological diseases contrasts with the dispersion of systemic and metabolic disorders, underscoring both the promise and limits of blood-based approaches for disease classification. This network physiology framework highlights the potential of integrating routine laboratory data with computational methods to refine disease ontology, map comorbidities, and advance precision diagnostics.