Personality traits like neuroticism and introversion are significant predictors of depression across the lifespan, according to research published in the Journal of Affective Disorders.
Depression remains one of the leading causes of disability worldwide. While depression is often characterized by a range of symptoms that vary widely among individuals, one consistent factor appears to be the role of personality traits. Previous research has established that certain personality traits, such as high neuroticism and low conscientiousness, are associated with increased risk for both depression and anxiety. However, many studies have either focused solely on one personality dimension at a time or have not considered the potential changes in these relationships across different stages of life.
Zhen Yang and colleagues sought to build on this body of work by examining the connections between personality traits and depressive and anxiety symptoms across the lifespan.
The researchers used data from the Nathan Kline Institute Rockland Sample (NKI-RS), a large, community-based dataset that includes individuals from diverse age groups, spanning from adolescence to older adulthood. The final sample included 1,494 participants aged 12 to 85 years. Participants were grouped into four categories: those with depression alone, those with anxiety alone, individuals with both depression and anxiety, and a healthy control group without any psychiatric conditions.
The psychiatric diagnoses for participants were determined using structured clinical interviews: the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS) for participants aged 6 to 17 and the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I/NP) for those aged 18 to 85.
The study assessed personality traits using the NEO Five-Factor Inventory (NEO-FFI), which evaluates five major dimensions of personality: neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness. The researchers also collected a range of psychological and physical measures, including cognitive tasks, questionnaires on eating behavior, and physiological evaluations such as heart rate monitoring and body mass index (BMI).
Individuals with depression or anxiety were found to have higher levels of neuroticism and lower levels of extraversion compared to healthy controls. This pattern was particularly pronounced in individuals with comorbid depression and anxiety, who displayed an even more vulnerable personality profile characterized by high neuroticism and greater introversion. In adolescence, depression was associated with higher neuroticism and lower levels of extraversion, agreeableness, and conscientiousness. Anxiety in adolescence, on the other hand, was linked to higher neuroticism and conscientiousness.
In adulthood, the relationship between personality traits and mental health symptoms appeared to change. Depression was no longer significantly associated with any personality traits after controlling for anxiety, suggesting that other factors, such as life stressors or biological changes, might play a more prominent role in influencing depression in adults.
However, anxiety remained strongly linked to neuroticism and was negatively associated with extraversion, agreeableness, and conscientiousness, indicating that more introverted, less agreeable, and less conscientious individuals are more likely to experience anxiety symptoms in adulthood.
In older adulthood, depression continued to show no significant correlation with personality traits, while anxiety remained associated with neuroticism and, uniquely in this age group, with agreeableness and openness to experience.
The machine learning model achieved a prediction accuracy of 70% for depression, with neuroticism and introversion emerging as the most significant predictors of depression. It further highlighted that a higher BMI, reduced heart rate variability during exercise, and certain eating behaviors, such as increased disinhibition and hunger perception, were also important factors contributing to the likelihood of depression. These findings suggest that a combination of personality traits, physical health indicators, and lifestyle behaviors can effectively predict depression risk.
Of note is that the cross-sectional design of the study limits the ability to draw causal inferences about the relationship between personality traits and depression.
The study, “Personality traits as predictors of depression across the lifespan”, was authored by Zhen Yang, Allison Li, Chloe Roske, Nolan Alexander, and Vilma Gabbay.
