Discovering latent subtypes of preterm birth and genetic risk using tensor decomposition on electronic health records.

Preterm birth is a syndrome that is triggered by diverse biological pathways and presents with many comorbid diseases. Although twin studies reveal a substantial heritable component, the genetic mechanisms of preterm birth remain poorly understood. We hypothesize that refining the preterm birth phenotype will reveal sub-phenotypes associated with distinct genetic risk factors and potential treatments. Here, we leverage rich longitudinal data from electronic health records (EHRs) from over 60,000 individuals from two clinical sites. Using tensor decomposition, we uncover several latent factors (LFs) that capture coherent combinations of comorbidities (e.g., metabolic, inflammatory, and mental health) and temporal trajectories of preterm and term births. Similar LFs are discovered between the two sites, underscoring their interpretability. Machine learning models trained on LFs accurately predict preterm birth and perform comparably to models trained on the full EHR data. Integrating genome-wide genotyping for >2,200 individuals, we find robust associations of preterm birth risk with high polygenic burden for cardiovascular disease, type 2 diabetes and body mass index. Using LFs, we discover that these genetic signals are strongly and specifically associated with different subsets of the preterm birth cohort. For example, the polygenic diabetes risk is associated with a LF characterized by relevant metabolic disorders. In summary, our study integrates latent phenotypes discovered from large EHR datasets with genetic data to predict preterm birth risk, uncover disease subtypes and comorbidities that drive genetic associations, and delineate the mechanisms underlying the heterogeneity of this complex trait.