Phenotyping and Hierarchical Clustering in Genetically Unresolved DSD Identifies Distinct Multisystem Clinical Subgroups – A DSDgene study

Abstract Context A substantial proportion of individuals with differences of sex development (DSD) remain without an identifiable molecular diagnosis. Systematic phenotyping may reveal clinically relevant patterns and inform genomic investigation. Objective To identify phenotypic patterns among individuals with genetically unresolved DSD and determine whether multisystem anomalies form distinct clinical clusters and exhibit overlap with established DSD-associated conditions. Design Data from the I-DSD Registry were analysed for individuals with 46,XY or 46,XX DSD lacking a molecular diagnosis. A three-part approach was used: (1) unsupervised hierarchical clustering based on Gower distance; (2) supervised phenotype-guided classification using reference profiles for NR5A1/SF-1 deficiency and P450 oxidoreductase deficiency (PORD); and (3) exploratory comparison with Human Phenotype Ontology (HPO)-derived profiles of established DSD-associated genes. Results Among 1,391 screened individuals, 345 (24.8%) fulfilled inclusion criteria, including the presence of at least one additional organ anomaly, 83.8% had a 46,XY karyotype. The most frequent associated findings were small for gestational age (28.1%), cardiac anomalies (21.4%), central nervous system (15.7%), and renal anomalies (14.5%). Unsupervised clustering identified five phenotypic groups, including craniofacial-dominant and central nervous system-dominant clusters. Phenotype-guided classification showed overlap with SF-1-deficiency-associated phenotypes in 11% of cases and with PORD-associated phenotypes in 22%. Comparison with HPO-derived profiles of 81 DSD-associated genes demonstrated phenotypic similarities between several clusters and known gene-associated anomaly spectra. Conclusions Systematic phenotyping identifies recognizable patterns of multisystem involvement in genetically unresolved DSD. Several clusters overlap with established DSD-associated conditions and gene-specific anomaly profiles, supporting phenotype-driven surveillance, multidisciplinary care, and informing future genomic re-evaluation strategies.