||Background: Given the strong relationship between depression and anxiety, there is an urge to investigate their
shared and specific long-term course determinants. The current study aimed to identify and compare the main
determinants of the 9-year trajectories of combined and pure depression and anxiety symptom severity.
Methods: Respondents with a 6-month depression and/or anxiety diagnosis (n=1,701) provided baseline data
on 152 sociodemographic, clinical and biological variables. Depression and anxiety symptom severity assessed at
baseline, 2-, 4-, 6- and 9-year follow-up, were used to identify data-driven course-trajectory subgroups for
general psychological distress, pure depression, and pure anxiety severity scores. For each outcome (classprobability), a Superlearner (SL) algorithm identified an optimally weighted (minimum mean squared error)
combination of machine-learning prediction algorithms. For each outcome, the top determinants in the SL were
identified by determining variable-importance and correlations between each SL-predicted and observed
outcome (?pred) were calculated.
Results: Low to high prediction correlations (?pred: 0.41-0.91, median=0.73) were found. In the SL, important
determinants of psychological distress were age, young age of onset, respiratory rate, participation disability,
somatic disease, low income, minor depressive disorder and mastery score. For course of pure depression and
anxiety symptom severity, similar determinants were found. Specific determinants of pure depression included
several types of healthcare-use, and of pure-anxiety course included somatic arousal and psychological distress.
Limitations: Limited sample size for machine learning.
Conclusions: The determinants of depression- and anxiety-severity course are mostly shared. Domain-specific
exceptions are healthcare use for depression and somatic arousal and distress for anxiety-severity course.