Personality traits as predictors of depression across the lifespan

Background: Depression is a major public health concern. A barrier for research has been the heterogeneous nature of depression, complicated by the categorical diagnosis of depression which is based on a cluster of symptoms, each with its own etiology. To address the multifactorial etiology of depression and its high comorbidity with anxiety, we aimed to examine the relations between personality traits, diverse behavioral, cognitive and physical measures, and depression and anxiety over the lifespan. Method: Our sample was drawn from the NKI-RS, a community-based lifespan sample (N = 1494 participants aged 6 to 85). Analyses included multivariate approach and general linear models for group comparisons and dimensional analyses, respectively. A machine learning model was trained to predict depression using many factors including personality traits. Results: Depression and anxiety were both characterized by increased neuroticism and introversion, but did not differ between themselves. Comorbidity had an additive effect on personality vulnerability. Dimensionally, depression was only associated with personality in adolescence, where it was positively correlated with neuroticism, and negatively correlated with extraversion, agreeableness, and conscientiousness. The relationship between anxiety and personality changed over time, with neuroticism and conscientiousness being the most salient traits. Our machine learning model predicted depression with 70 % accuracy with neuroticism and extraversion contributing most. Limitations: Due to the cross-sectional design, conclusions cannot be drawn about causal relationships between personality and depression. Conclusion: These results underscore the impact of personality on depressive disorders and provide novel insights on how personality contributes to depression across the lifespan.