Parsing the Pediatric Mood Spectrum in U.S. Youth: Transdiagnostic Severity Versus Phenotypic Specificity

Objective: Emotion dysregulation and irritability occur across pediatric disorders, complicating distinctions between general symptom burden and specific clinical presentations. Person-centered analyses can look at profiles of scores to identify groups with similar presentations. We examined whether modeling shared and domain-specific symptom variance identified differentiated profiles in youth. Method: Caregivers of 2,187 US youth ages 6-18 completed 87 items assessing emotion dysregulation, depression, mania-related symptoms, sleep-related impairment, and attention and behavioral problems. An ordinal bifactor model estimated one general and 13 specific factors. Latent profile analyses used standardized scores on all 14 factors, with an expanded raw-score analysis as a comparator. We examined caregiver-reported diagnoses and functioning and approximately two-week classification agreement among 166 recontacted caregivers. Results: The general factor accounted for 62% of common variance ({omega}H=.94), although model fit was mixed (CFI=.884; RMSEA=.058; SRMR=.092). A provisional four-class solution distinguished population-modal (67.4%), internalizing (11.8%), attention/hyperactivity (11.0%), and unusual-thought/elevated-mood (9.8%) profiles. Entropy was .83, but model enumeration and initialization checks did not establish a unique solution. Profiles showed overlapping diagnostic and functional correlates. Recontact agreement was 78.9% ({kappa}=.60). Irritability-specific reliability was limited, constraining interpretation of its weak profile differentiation. Conclusions: General symptom burden coexisted with differentiated residual symptom patterns. These findings support evaluating severity alongside symptom configuration, without equating profiles with diagnoses or weak residual irritability signals with limited clinical relevance. Measurement refinement, model-selection verification, and confirmation of classification-error-adjusted associations are needed before clinical application, but profiles could simplify clinical interpretation of sets of scales.