A Multi‐Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views

• DOI: 10.1002/advs.202401754
• Publication Date: 2024-06-06T05:40:15Z
• Authors: Lena Möbus; Angela Serra; Michele Fratello; Alisa Pavel; Antonio Federico; Dario Greco
• Keywords: 0301 basic medicine; 570; Bioinformatics; Science; multi‐dimensional; disease mapping; 610; disease clustering; Computational biology; 03 medical and health sciences; computational biology; disease similarity; Alzheimer Disease; International Classification of Diseases; 319; Disease clustering; Humans; Cluster Analysis; Disease; /dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being; name=SDG 3 - Good Health and Well-being; systems medicine; multi-dimensional; Q; Multi‐dimensional; bioinformatics; 3111; Biomedicine; Phenotype; Diabetes Mellitus, Type 2; Disease similarity; Systems medicine; Disease mapping; Multi-dimensional; Research Article

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This limiting view to pathological manifestations, while it neglects mechanistic relationships that are crucial develop therapeutic strategies. work aims advance understanding their relatedness beyond traditional views. Hence, similarity among 502 mapped using six different data dimensions encompassing molecular, clinical, pharmacological information retrieved from public sources. Multiple distance measures multi-view clustering used assess patterns disease relatedness. integration all into a consensus map reveals divergent International Classification Diseases (ICD), emphasizing novel insights offered by map. Disease features such as genes, pathways, chemicals enriched in distinct groups identified. Finally, an evaluation top similar three candidate common Western population shows concordance with known epidemiological associations rare shared between Type 2 diabetes (T2D) Alzheimer's disease. A revision holds promise for facilitating reconstruction comorbidity patterns, repurposing drugs, advancing drug discovery future.