Background: The mechanisms underlying the high comorbidity between internalizing disorders (IDs) and functional disorders (FDs) remain unclear. This study aimed to identify data-driven subgroups of major depressive disorder (MDD), generalized anxiety disorder (GAD), myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), fibromyalgia (FM), and irritable bowel syndrome (IBS) symptoms in the general population, capturing shared symptom patterns while accounting for variation in symptom severity.
Method: We analyzed cross-sectional data from 72,919 adults in the Dutch Lifelines Cohort Study, with participants randomly divided into training (n=36,459) and validation (n=36,460) subsets. Twenty-seven symptoms from the ID and FD diagnostic criteria were examined using latent class analysis and mixed-measurement item response theory (MM-IRT) in the training set, and the optimal MM-IRT model was validated in the validation set. Class characteristics were then assessed by examining associations with known risk factors for IDs and FDs (e.g., demographics, chronic stress, BMI), along with ID and FD diagnosis and comorbidity patterns.
Results: Six classes best described the data: Healthy (57.5%), Pain (13.8%), Tension/Pain (9.1%), Anxiety (9.0%), Cognition/Fatigue (6.4%), and Depression (4.3%). All classes showed a combination of ID and FD symptoms, with the Cognition/Fatigue and Depression classes being the most mixed, and the Pain and Anxiety classes the most domain-specific. Transdiagnostic symptoms were endorsed at relatively high levels across all classes. ID–FD comorbidity was highest in the Cognition/Fatigue and Depression classes, with the Depression class also showing the greatest functional impairment.
Conclusions: The identified classes comprised mixed symptoms from multiple disorders, rather than disorder-specific, pure classes. ID–FD comorbidity may partly arise from both shared structural features of their current diagnostic criteria and from shared etiological mechanisms.
Data-driven subtypes of internalizing and functional somatic symptoms: a hybrid mixture modeling approach
Year of publication
2026
Journal
General hospital psychiatry
Author(s)
Saini, U.
Wanders, R.
Oldehinkel, A.J.
Rosmalen, J.G.M.
van Loo, H.M.
Full publication
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