Characterizing Autism Phenotype Onset in the Electronic Health Records to Enable Age-Specific Screening

Autism exhibits substantial phenotypic heterogeneity, yet age-dependent variation in phenotypic profiles and onset timing remains poorly understood. We aimed to characterize stage-specific phenotypes and temporal onset patterns of autism using longitudinal electronic health records (EHRs). We analyzed EHR data from Columbia University Irving Medical Center. Autism cases were stratified by age at diagnosis. Phenotype enrichment analyses were performed using chi-square and Fisher's exact tests to identify phenotypes overrepresented in autism cases compared with matched controls, and temporal patterns of phenotype onset were subsequently evaluated to identify stage-specific clinical signatures and provide insight into differences between early- and late-diagnosed individuals. Among 4818 cases and 1433 phenotypes, 195 and 155 were enriched in toddlerhood and adolescence groups. Early diagnosed individuals exhibited a higher prevalence of sensory-related conditions, greater exposure to maternal and perinatal complications than their matched controls (all ORs > 1; p < 0.01), and earlier onset of neurologic features, including coordination abnormalities and convulsions, compared with later-diagnosed individuals. The study provides a publicly accessible resource to enrich autism-related phenotypic profiles, uncovering notable phenotypic differences and variations in timing of phenotype onset between early-diagnosed and later-diagnosed individuals. These findings underscore phenotypic heterogeneity and highlight the need for age-sensitive screening strategies. To our knowledge, this is among the first studies to jointly characterize phenotypic profiles and their temporal onset patterns across developmental stages in autism using longitudinal EHR data. These findings offer novel insights into age-dependent clinical signatures, informing earlier and more precise screening strategies.