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Identification of distinct clinical phenotypes and their neurobiological signatures in stress-exposed individuals: A multimodal machine learning approach
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DOI：10.1192/j.eurpsy.2026.12225.png)
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Abstract
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BackgroundIndividual responses to stress are highly heterogeneous; resulting in diverse psychopathological outcomes. This variability poses challenges for traditional diagnostic frameworks and underscores the need for a transdiagnostic approach to guide interventions. This study aimed to identify distinct phenotypes within a stress-exposed population and to characterize their biological profiles using a multimodal machine learning framework.MethodsA total of 809 stress-exposed adults (mean age 40.5 ± 8.74 years; 53.7% female) underwent clinical; laboratory; and structural MRI assessments. Data-driven clustering of clinical variables identified phenotypes; followed by machine learning classifiers trained on neuroimaging and laboratory data to predict phenotype membership. SHapley Additive exPlanations (SHAP) analysis was used to identify key biological features distinguishing each phenotype.ResultsThree phenotypes were identified: a multi-risk group (n = 321) characterized by prominent depression; anxiety; and sleep disturbances; an alcohol-related risk group (n = 226) with high alcohol misuse and minimal comorbidity; and a resilient low-risk group (n = 262). Machine learning models accurately classified these phenotypes; indicating distinct biological profiles. SHAP analysis revealed phenotype-specific signatures: the multi-risk phenotype was associated with frontal-subcortical structural alterations and dysregulated cortisol; whereas the alcohol-related risk phenotype was characterized by frontal-insular structural alterations and metabolic abnormalities.ConclusionsThis study demonstrates the stratification of stress-exposed individuals into clinically and biologically distinct phenotypes. By integrating multimodal data with machine learning; we identified phenotype-specific neurobiological and metabolic profiles that extend beyond conventional diagnostic frameworks. These findings support a transdiagnostic; data-driven approach to improve risk stratification and inform personalized interventions in stress-exposed populations.
Keywords:
machine learning
neuroimaging
phenotype
stress-exposed individuals
transdiagnostic clustering
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