Citation

Betz LT, Rosen M, Salokangas RKR, Kambeitz J. J. Affect. Disord. 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.jad.2022.07.042

PMID

35882299

Abstract

BACKGROUND: Different types of childhood maltreatment (CM) are key risk factors for psychopathology. Specifically, there is evidence for a unique role of emotional abuse in affective psychopathology in children and youth; however, its predictive power for depressive symptomatology in adulthood is still unknown. Additionally, emotional abuse encompasses several facets, but the strength of their individual contribution to depressive affect has not been examined.

METHOD: Here, we used a machine learning (ML) approach based on Random Forests to assess the performance of domain scores and individual items from the Childhood Trauma Questionnaire (CTQ) in predicting self-reported levels of depressive affect in an adult general population sample. Models were generated in a training sample (N = 769) and validated in an independent test sample (N = 466). Using state-of-the-art methods from interpretable ML, we identified the most predictive domains and facets of CM for adult depressive affect.

RESULTS: Models based on individual CM items explained more variance in the independent test sample than models based on CM domain scores (R(2) = 7.6 % vs. 6.4 %). Emotional abuse, particularly its more subjective components such as reactions to and appraisal of the abuse, emerged as the strongest predictors of adult depressive affect. LIMITATIONS: Assessment of CM was retrospective and lacked information on timing and duration. Moreover, reported rates of CM and depressive affect were comparatively low.

CONCLUSIONS: Our findings corroborate the strong role of subjective experience in CM-related psychopathology across the lifespan that necessitates greater attention in research, policy, and clinical practice.

Language: en

Keywords

Depression; Adverse childhood experiences; Childhood maltreatment; Childhood trauma; Early adversity; Precision medicine