A new computational phenotyping framework for the clinical characterization of pediatric celiac disease

Pediatric celiac disease (CD) is characterized by marked clinical and immunological heterogeneity that is not fully captured by the current symptom-based Oslo classification. Data-driven computational phenotyping may enable the identification of more homogeneous patient subgroups by integrating multimodal clinical information. This study proposes a topological data analysis (TDA)-based computational framework (pheTDA) to identify and characterize novel sub-phenotypes of pediatric CD from multicentric clinical data.

A retrospective multicentric cohort of 2,922 pediatric CD patients described by 34 demographic, clinical, serological, genetic, and histological variables was analyzed. Mixed-type data were processed using Gower distance and mapped through a semi-supervised TDA Mapper pipeline, followed by Louvain community detection. The resulting communities were characterized through statistical testing and supervised machine learning. pheTDA was benchmarked against t-SNE followed by DBSCAN and agglomerative hierarchical clustering (AHC), and sensitivity analyses repeated pheTDA using clinical variables only and diagnostic-test variables only.

pheTDA identified seven relatively balanced sub-phenotypes with distinct combinations of symptoms, serological profiles, histological damage, growth delay, and autoimmune comorbidities. Compared with pheTDA, DBSCAN generated a markedly imbalanced nine-cluster partition, whereas the four-cluster AHC solution was dominated by sex. pheTDA showed a clinically broader effect-size profile, with its leading discriminators spanning clinical, serological, genetic, and histological domains. Clinical-only pheTDA communities were strongly aligned with the symptom-based Oslo categories, whereas diagnostic-test-only communities were more fragmented and reflected specific test combinations. Random Forest classifiers achieved a higher mean AUC for pheTDA communities than for Oslo classes (0.94 vs. 0.88).

Multimodal pheTDA provides an exploratory and reproducible framework for identifying clinically interpretable pediatric CD sub-phenotypes. Benchmarking and feature-domain sensitivity analyses support the value of integrating clinical and diagnostic information while emphasizing that the resulting communities are data-dependent computational representations rather than definitive disease classes.