AI Insight
This study analyzed genetic risk scores for 25 psychiatric and health conditions against 84 laboratory measurements in over 45,000 individuals to identify biological signatures of mental health disorders. Researchers found distinct physiological profiles for different psychiatric conditions: anxiety, ADHD, depression, and PTSD shared metabolic and inflammatory markers (elevated glucose, BMI, and monocytes; reduced HDL cholesterol); anorexia, OCD, and schizophrenia showed lower BMI; while bipolar disorder exhibited reduced electrolytes and kidney function markers. Substance use disorders showed similar clustering patterns, with tobacco use linked to metabolic dysfunction and alcohol use associated with lower red blood cell measures.
Why it matters
This research could help develop objective laboratory-based biomarkers for psychiatric disorders, which currently rely solely on behavioral symptoms for diagnosis. The identification of distinct physiological signatures may improve early detection, enable personalized treatment approaches, and reveal new biological pathways that could be targeted for therapeutic intervention.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Background: Psychiatric disorders pose a significant public health burden, and their complex genetic architecture underlies multiple biological pathways, contributing to a lack of diagnostic biomarkers. Laboratory-Wide Association Studies (LabWAS) leverage electronic health record laboratory data as intermediary phenotypes to link genetic risk to physiology. Methods: Polygenic Scores (PGS) for 25 health-related, psychiatric, and substance use phenotypes were generated for individuals of African (AFR; N=10,473) and European (EUR, N=35,131) ancestry from the Penn Medicine Biobank. Laboratory data was prepared using the LabWAS pipeline, encompassing 78 laboratory tests and 6 vital signs. Linear regression models tested the association of each lab value with PGS adjusting for sex, age, and ten within-ancestry genetic principal components. Results: Four PGS were significantly associated with at least one laboratory value in AFR individuals and 22 PGS in EUR individuals. In EUR individuals, psychiatric PGS partitioned into three physiological profiles: anxiety, attention-deficit/hyperactivity disorder, major depressive disorder, and post-traumatic stress disorder shared a metabolic/inflammatory signature (higher BMI, glucose, and monocytes; lower high density lipoprotein cholesterol and oxygen saturation); anorexia nervosa, obsessive compulsive disorder, and schizophrenia were characterized by lower BMI; and bipolar disorder demonstrated a more circumscribed profile with lower electrolytes and renal makers. Substance use disorder PGS similarly clustered: tobacco use disorder and externalizing showed metabolic/inflammatory profiles; alcohol-related phenotypes showed lower red blood cell indices; and cannabis use disorder had a more circumscribed profile. Conclusions: LabWAS reveals physiologic signatures associated with genetic risk for psychiatric disorders, offering new biological insights into their pathology.