Identification of multimodal mental health signatures in the young population using deep phenotyping
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Background
Mental health encompasses emotional, psychological, and social dimensions, extending beyond the mere absence of illness. Shaped by a complex interplay of hereditary factors and life experiences, mental health can deteriorate into clinical conditions necessitating intervention. However, the ambiguity between pathological and non-pathological states, along with overlapping clinical profiles, challenges traditional diagnostic procedures, highlighting the need for a dimensional approach in stratified psychiatry.
Methods
We analyzed comprehensive phenotypic data from ∼300 young Danish participants, including psychometric assessments, brain imaging, genetics, and circulatory OMICs markers. Using a novel psychometry-based archetyping approach, we employed soft-clustering analyses to stratify participants based on distinct cognitive, emotional, and behavioral patterns, while exploring their genetic and neurobiological underpinnings.
Results
Five psychometric archetypes were identified, representing a continuum of mental health traits. One archetype, characterized by high neuroticism, emotional dysregulation, and elevated stress and depression scores, was firmly associated with self-reported mental health diagnoses, psychiatric comorbidities, and family history of mental illness. Genetic predisposition to mental health conditions, reflected in polygenic scores (PGSs), accounted for up to 9% of the variance in archetypes, with significant contributions from neuroimaging-related PGSs. The overlaps between broader genetic profiles and archetypes further confirmed their biological foundations. Neuroimaging data linked the risk-associated archetype to both regional and global brain volumetric changes, while metabolomic analysis identified differentiating metabolites related to mood regulation and neuroinflammation.
Conclusions
This study demonstrates the feasibility of data-driven stratification of the general population into distinct risk groups defined by multimodal mental health signatures. This stratification offers a robust framework for understanding mental health variation and holds significant potential for advancing early screening and targeted intervention strategies in the young population.
