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

> **NIH NIH K08** · MASSACHUSETTS GENERAL HOSPITAL · 2021 · $197,704

## Abstract

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 scien...

## Key facts

- **NIH application ID:** 10284131
- **Project number:** 1K08MH127413-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Karmel Choi
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $197,704
- **Award type:** 1
- **Project period:** 2021-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10284131

## Citation

> US National Institutes of Health, RePORTER application 10284131, Integrating Health Records, Genomic, and Social Data to Stratify Adolescent Depression Risk (1K08MH127413-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10284131. Licensed CC0.

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