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Original Investigation
March 31, 2021

Discriminating Heterogeneous Trajectories of Resilience and Depression After Major Life Stressors Using Polygenic Scores

Author Affiliations
  • 1Department of Emergency Medicine, Columbia University Irving Medical Center, New York, New York
  • 2Data Science Institute, Columbia University, New York, New York
  • 3Department of Psychiatry, NYU Grossman School of Medicine, New York, New York
  • 4Department of Psychiatry, Massachusetts General Hospital, Boston
  • 5Department of Psychiatry, NYU Grossman School of Medicine, New York, New York
  • 6Teachers College, Columbia University, New York, New York
JAMA Psychiatry. Published online March 31, 2021. doi:10.1001/jamapsychiatry.2021.0228
Key Points

Question  Is it possible to accurately discriminate longitudinal trajectories of depression and resilience by using multiple polygenic scores as psychiatric risk and health indicators?

Findings  In this longitudinal cohort study including 2071 participants, resilience and symptomatic trajectories were accurately discriminated using 21 polygenic scores using deep neural nets. The resilience trajectory was associated with lower polygenic scores for several psychiatry disorders as well as metabolic risk.

Meaning  The results of this study suggest that polygenic scores can be used to determine long-term risk for depression and resilience.

Abstract

Importance  Major life stressors, such as loss and trauma, increase the risk of depression. It is known that individuals show heterogeneous trajectories of depressive symptoms following major life stressors, including chronic depression, recovery, and resilience. Although common genetic variation has been associated with depression risk, genomic factors that could help discriminate trajectories of risk vs resilience following adversity have not been identified.

Objective  To assess the discriminatory accuracy of a deep neural net combining joint information from 21 psychiatric and health-related multiple polygenic scores (PGSs) for discriminating resilience vs other longitudinal symptom trajectories with use of longitudinal, genetically informed data on adults exposed to major life stressors.

Design, Setting, and Participants  The Health and Retirement Study is a longitudinal panel cohort study in US citizens older than 50 years, with data being collected once every 2 years between 1992 and 2010. A total of 2071 participants who were of European ancestry with available depressive symptom trajectory information after experiencing an index depressogenic major life stressor were included. Latent growth mixture modeling identified heterogeneous trajectories of depressive symptoms before and after major life stressors, including stable low symptoms (ie, resilience), as well as improving, emergent, and preexisting/chronic symptom patterns. Twenty-one PGSs were examined as factors distinctively associated with these heterogeneous trajectories. Local interpretable model-agnostic explanations were applied to examine PGSs associated with each trajectory. Data were analyzed using the DNN model from June to July 2020.

Exposures  Development of depression and resilience were examined in older adults after a major life stressor, such as bereavement, divorce, and job loss, or major health events, such as myocardial infarction and cancer.

Main Outcomes and Measures  Discriminatory accuracy of a deep neural net model trained for the multinomial classification of 4 distinct trajectories of depressive symptoms (Center for Epidemiologic Studies–Depression scale) based on 21 PGSs using supervised machine learning.

Results  Of the 2071 participants, 1329 were women (64.2%); mean (SD) age was 55.96 (8.52) years. Of these, 1638 (79.1%) were classified as resilient, 160 (7.75) in recovery (improving), 159 (7.7%) with emerging depression, and 114 (5.5%) with preexisting/chronic depression symptoms. Deep neural nets distinguished these 4 trajectories with high discriminatory accuracy (multiclass micro-average area under the curve, 0.88; 95% CI, 0.87-0.89; multiclass macro-average area under the curve, 0.86; 95% CI, 0.85-0.87). Discriminatory accuracy was highest for preexisting/chronic depression (AUC 0.93), followed by emerging depression (AUC 0.88), recovery (AUC 0.87), resilience (AUC 0.75).

Conclusions and Relevance  The results of the longitudinal cohort study suggest that multivariate PGS profiles provide information to accurately distinguish between heterogeneous stress-related risk and resilience phenotypes.

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