Psychopharmacolipidomics: Understanding the Global Impact of Psychopharmacologic Treatments on Plasma Lipidomic Profiles

ObjectiveThis study aimed to investigate the naturalistic effects of psychopharmacologic treatments on the plasma lipidome using a transdiagnostic approach, focusing on antipsychotics and antidepressants. MethodsUsing liquid chromatography-mass spectrometry, we analyzed untargeted plasma lipidomic profiles of 622 individuals (192 control individuals, 187 with schizophrenia, 243 with bipolar disorder) who participated in the naturalistic PsyCourse Study, a longitudinal deep-phenotyping study focussing on affective and non-affective psychoses recruited across Germany and Austria. Differential lipid species, class enrichments, and clustering patterns were examined using tools like match.it, lipidr and clustering methods in R to investigate associations between the plasma lipidome and different psychopharmacologic treatments. ResultsThe lipid class enrichment analysis revealed that antipsychotics induced the most pronounced lipid alterations, affecting six classes including triacylglycerides (TAG), acylcarnitines (CAR), and diacylglycerols (DAG). TAGs were increased, while fatty acids (FA) were consistently decreased with both antipsychotic and antidepressant treatment. Although antipsychotics, antidepressants, and tranquilizers showed overall class effects, subgroups displayed differential, sometimes opposing patterns. The "ziprasidone-like" group showed increased sphingomyelins (dSM) and decreased FA. Antidepressants primarily affected TAG and phosphatidylcholines (PC), with SSRI and SNRI subgroups showing significant enrichment. Unbiased hierarchical clustering based on sex, age, BMI, diagnosis, and the use of individual drugs revealed a cluster of severely affected individuals characterized by key lipid species (dSM 39:2, FA 26:4, CAR 14:0). ConclusionPsychopharmacologic treatments, particularly antipsychotics and antidepressants, significantly alter plasma lipid profiles, with specific lipid classes like TAGs and FAs affected consistently across medication groups but also class- and subgroup-specific effects, yielding insights into potential mechanisms of action, drug response, and unwanted effects.