Demonstrating the potential of untargeted hair proteomics for personalized biomarkers in stress-associated disorders

Biomarker research in psychopathology increasingly employs high-dimensional omics approaches. Yet, proteomics based on human hair remain largely unexplored, despite its potential to efficiently capture stable biological signals accumulated over weeks to months. This study leveraged machine learning to investigate the potential of the hair proteome--all detectable peptides and proteins--as a biomarker source for stress-associated psychopathology. We analyzed protein profiles from hair segments of women with non-suicidal self-injury disorder (n = 36) and healthy controls (n = 32). Of 1114 identified proteins, 611 were sufficiently abundant for analyses. Partial Least Squares Discriminant Analysis achieved stable 84.4% cross-validated accuracy for classification of clinical groups (p < .001), outperforming models based on data-derived clusters (60%), stress-related proteins (73%), and simulated hair cortisol from meta-analytic effect sizes (53-59%). Predicted class probabilities strongly correlated with clinical symptoms and well-being (r > .60). Key predictive proteins were linked to pain perception, oxidative stress, and cholesterol homeostasis. Approximately 15% of proteins differed significantly between groups, with the strongest candidates related to ribosomal function--an emerging target in depression. These findings establish hair proteomics as a promising, non-invasive biomarker source for psychiatric research, warranting validation in larger cohorts and exploration of clinical applications in risk assessment and personalized interventions.