Precision Metabolic Risk Stratification in Depression via Multi-Omics and EHR - Major depressive disorder (MDD) represents a critical but overlooked driver of type 2 diabetes (T2D) risk, with studies showing 60% increased T2D incidence among individuals with MDD. This risk is further amplified by antidepressants, particularly selective serotonin reuptake inhibitors (SSRIs), which carry largely unmonitored metabolic effects in routine psychiatric care. Despite this dual burden, three interconnected translational gaps prevent effective T2D prevention in this high-risk population. First, while polygenic and metabolomic risk scores effectively predict T2D in general populations, they remain unvalidated in psychiatric populations. Second, SSRIs lack systematic metabolic monitoring despite emerging evidence of risk. Third, no clinically deployable tools exist to integrate multi-omics insights with clinical data, leaving clinicians without evidence-based frameworks for T2D risk stratification at the critical point of MDD diagnosis. To address these gaps, Dr. Lee will leverage large-scale health system biobanks to develop a comprehensive framework for T2D risk assessment in psychiatric populations. Her approach recognizes that T2D risk in MDD patients originates from two distinct sources: (i) baseline biological susceptibility and (ii) medication-induced metabolic sensitivity. Aim 1 will validate multi-omics risk scores, including pathway-specific polygenic scores and metabolomic signatures, in SSRI-naïve MDD patients to establish baseline susceptibility for T2D. Aim 2 will characterize SSRI-induced metabolic sensitivity by tracking early biochemical changes within the first year of MDD diagnosis, identifying biomarkers particularly sensitive to SSRI exposure. Aim 3 will integrate these multimodal insights into transparent, clinically implementable risk stratification models, with external validation ensuring generalizability across distinct healthcare settings. This systematic approach will transform T2D prevention by providing evidence-based tools for metabolic risk assessment at MDD diagnosis, establishing the scientific foundation for monitoring guidelines and precision prevention strategies in psychiatric care. Dr. Lee's expertise in epidemiology, causal inference, and statistical genetics uniquely positions her to bridge psychiatric and metabolic medicine. Her comprehensive training plan integrates coursework across endocrinology, metabolic genetics, precision medicine, and biomedical informatics, supported by a multidisciplinary mentorship team spanning these critical domains. Primary mentor Dr. Jordan Smoller (precision medicine), co-mentor Dr. Miriam Udler (diabetes genetics), and collaborators Drs. Melina Claussnitzer (metabolic genomics), Arjun Manrai (biomedical informatics), and Lea Davis (psychiatric genetics) provide expertise across the full translational spectrum from genomic discovery to clinical implementation. This K01 will establish Dr. Lee as an independent investigator at the intersection of metabolic and mental health, advancing precision prevention strategies for T2D this high-risk population.
