Genetic Correlates of Treatment-Resistant Depression: Insights from Polygenic Scores Across Cognitive, Temperamental, and Sleep Traits in the All of US cohort

Abstract

BackgroundTreatment-resistant depression (TRD) is a major challenge in mental health, affecting a significant number of patients and leading to considerable economic and social burdens. The etiological factors contributing to TRD are complex and not fully understood.ObjectiveTo investigate the genetic factors associated with TRD using polygenic scores (PGS) across various traits, and to explore their potential role in the etiology of TRD using large-scale genomic data from the All of Us Research Program (AoU).MethodsData from 292,663 participants in the AoU were analyzed using a case-cohort design. Treatment resistant depression (TRD), treatment responsive Major Depressive Disorder (trMDD), and all others who have no formal diagnosis of Major Depressive Disorder (non-MDD) were identified through diagnostic codes and prescription patterns. Polygenic scores (PGS) for 61 unique traits from seven domains were used and logistic regressions were conducted to assess associations between PGS and TRD. Finally, Cox proportional hazard models were used to explore the predictive value of PGS for progression rate from the diagnostic event of Major Depressive Disorder (MDD) to TRD.ResultsIn the discovery set (104128 non-MDD, 16640 trMDD, and 4177 TRD), 44 of 61 selected PGS were found to be significantly associated with MDD, regardless of treatment responsiveness. Eleven of them were found to have stronger associations with TRD than with trMDD, encompassing PGS from domains in education, cognition, personality, sleep, and temperament. Genetic predisposition for insomnia and specific neuroticism traits were associated with increased TRD risk (OR range from 1.05 to 1.15), while higher education and intelligence scores were protective (ORs 0.88 and 0.91, respectively). These associations are consistent across two other independent sets within AoU (n = 104,388 and 63,330). Among 28,964 individuals tracked over time, 3,854 developed TRD within an average of 944 days (95% CI: 883 ∼ 992 days) after MDD diagnosis. All eleven previously identified and replicated PGS were found to be modulating the conversion rate from MDD to TRD. Thus, those having higher education PGS would experiencing slower conversion rates than those who have lower education PGS with hazard ratios in 0.79 (80thversus 20thpercentile, 95% CI: 0.74 ∼ 0.85). Those who had higher insomnia PGS experience faster conversion rates than those who had lower insomnia PGS, with hazard ratios in 1.21 (80thversus 20thpercentile, 95% CI: 1.13 ∼ 1.30).ConclusionsOur results indicate that genetic predisposition related to neuroticism, cognitive function, and sleep patterns play a significant role in the development of TRD. These findings underscore the importance of considering genetic and psychosocial factors in managing and treating TRD. Future research should focus on integrating genetic data with clinical outcomes to enhance our understanding of pathways leading to treatment resistance.Key PointsQuestionWhat are the predisposing characteristics among individuals who develop treatment-resistant depression (TRD)?FindingsAnalysis of data from 292,663 participants in the All of Us Research Program revealed that polygenic scores (PGS) for traits including neuroticism, cognitive function, and sleep patterns were significantly associated with major depressive disorder (MDD) and, particularly, with TRD. Among the 61 traits studied, 11 showed stronger associations with TRD compared to treatment responsive MDD, including traits linked to higher education and intelligence which appeared protective, and neuroticism and insomnia which increased risk.MeaningThe findings underscore the importance of considering predisposing factors when managing and treating TRD. They suggest potential intervening pathways through tailored approach with the identified predisposing characteristics, reducing the risk of progression to treatment resistance in depression. Personalized genetic information that measures the underlying predispositions could eventually enhance therapeutic strategies.