Identification of multimodal mental health signatures in the young population using deep phenotyping

Abstract

AbstractBackgroundMental 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.MethodsWe 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.ResultsFive 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.ConclusionsThis 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.