Integrating Health Records, Genomic, and Social Data to Stratify Adolescent Depression Risk

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PROJECT ABSTRACT One in five adolescents in the United States will experience a depressive episode before age 18. Early prevention could offset a lifetime of morbidity including work and social impairment, substance use, and suicidal behavior. A critical step to preventing adolescent depression at a population level is the efficient detection of individuals who could benefit most from targeted intervention. However, known risk factors (e.g., subthreshold symptoms, cognitive styles, interpersonal factors) are often not widely assessed in practice until young people are presenting for psychiatric care, and prospective risk screening tools built in traditional research studies remain poorly implemented at scale in clinical settings where it may not be feasible for providers to routinely collect or integrate additional measures. Large-scale, routine electronic health records (EHRs) from major health systems present a powerful opportunity to overcome these prior limitations but have not yet been harnessed for adolescent depression and often lack environmental and genetic data that may inform etiological understanding and risk stratification. The overall aim of this K08 Career Development Award is to leverage large-scale EHR data with linked genomic and social determinants to enhance the systematic identification of young people at elevated risk of depression in real-world health settings. In this project, the candidate will develop and validate a novel phenotype algorithm for identifying adolescent depression cases from a major healthcare system in the United States containing up to 20 years of longitudinal EHR data for over six million individuals (Aim 1); integrate and comprehensively assess a range of potential social and genomic determinants for EHR-based adolescent depression (Aim 2); and apply modern statistical and machine learning methods to train and evaluate an initial prospective risk stratification model for adolescent depression based on routine EHR data (Aim 3). Improving the phenotyping and stratification of adolescent depression in EHRs will facilitate new avenues of research that will be the basis of subsequent R-level grants that include external validation across health systems, refinement of risk stratification and clinical trajectory models, and brief preventive interventions to enhance resilience in those at risk. Supported by a solid foundation in psychiatric and genetic epidemiology and a multidisciplinary team of world-class experts in an ideal environment, the candidate will acquire new expertise in predictive analytics, biomedical informatics (specifically EHR-exposome-genome integration), adolescent depression and prevention science through intensive mentored research and supervised training and professional development activities. This Award will provide the necessary training for the candidate to develop into a fully independent clinically informed investigator with a translational research program that bridges data science, statistical genetics, and developmental epidemiology to inform actionable strategies for early depression prevention and resilience promotion.