Abstract Rationale Chronic respiratory disease affects many individuals born very preterm (≤32 weeks gestation). Prematurity-associated lung disease is complex and heterogenous, with individuals displaying phenotypic traits that mimic other respiratory conditions. A multidimensional model to determine individual phenotypes of lung diseases has been proposed as a first step in optimising the management of prematurity-associated lung disease. Topological data analysis is a framework of statistical methods, including cluster analysis, that can be used to identify unique groupings or “clusters” of individuals who share similarities, but are dissimilar to those in other clusters. This data-driven method can be used to identify phenotypes of disease. We aimed to identify novel phenotypes of prematurity-associated lung disease by applying Topological data analysis using the Mapper algorithm to urinary biomarkers. Methods Urine samples were collected from young-adults born very preterm (1997-2003) in Western Australia. Concentrations of urinary biomarkers (interleukin-8 AX 1.4±1.3) but not cluster 2 (Rrs5 1.2±0.9; AX 2.0±1.0). Antenatal corticosteroid use was lowest in cluster 1 (65%) compared to cluster 2 (92%) and 3 (91%). No differences were seen in other perinatal characteristics or lung function measures between groups. Conclusions Increased Rrs5 and AX z-scores, with lower levels of clara cell protein may indicate a small airway disease phenotype in this population. This study highlights the importance of using multiple biomarkers for diagnostic testing. This abstract is funded by: Curtin University, The Kids Research Institute Australia, Stan Perron Charitable Foundation, WA Child Research Fund
Bradshaw et al. (2026) studied this question.