Latent class analysis of psychotic-affective disorders with data-driven plasma proteomics

Abstract

Data-driven approaches to subtype transdiagnostic samples are important for understanding heterogeneity within disorders and overlap between disorders. Thus, this study was conducted to determine whether plasma proteomics-based clustering could subtype patients with transdiagnostic psychotic-affective disorder diagnoses. The study population included 504 patients with schizophrenia, bipolar disorder, and major depressive disorder and 160 healthy controls, aged 19 to 65 years. Multiple reaction monitoring was performed using plasma samples from each individual. Pathologic peptides were determined by linear regression between patients and healthy controls. Latent class analysis was conducted in patients after peptide values were stratified by sex and divided into tertile values. Significant demographic and clinical characteristics were determined for the latent clusters. The latent class analysis was repeated when healthy controls were included. Twelve peptides were significantly different between the patients and healthy controls after controlling for significant covariates. Latent class analysis based on these peptides after stratification by sex revealed two distinct classes of patients. The negative symptom factor of the Brief Psychiatric Rating Scale was significantly different between the classes (t = −2.070, p = 0.039). When healthy controls were included, two latent classes were identified, and the negative symptom factor of the Brief Psychiatric Rating Scale was still significant (t = −2.372, p = 0.018). In conclusion, negative symptoms should be considered a significant biological aspect for understanding the heterogeneity and overlap of psychotic-affective disorders.

Evaluation of a Nondepleted Plasma Multiprotein-Based Model for Discriminating Psychiatric Disorders Using Multiple Reaction Monitoring-Mass Spectrometry: Proof-of-Concept Study

“…Proteomics analysis is a powerful approach to mitigate conundrums of diseases in clinical research, which has been demonstrated in previous studies. − Particularly, targeted proteomics using multiple reaction monitoring-mass spectrometry (MRM-MS) is regarded as a powerful quantitative method that is advantageous compared with antibody-based assays in terms of multiplex ability, high throughput, and reproducibility. − Recently, we have been constructing diagnostic multiprotein models for various diseases using MRM-MS and machine learning (ML) methods. − When it comes to discrimination of psychiatric disorders, we previously developed models to distinguish between MDD, BD, and SCZ with high accuracy using MRM-MS in depleted plasma samples. , Although the constructed models had high distinguishability, the depletion process of high abundant proteins is time-consuming and labor-intensive, which takes around half of the total time of sample preparation. − In this study, we improved our LC-MRM-MS method to detect and quantify as many proteins as possible with nondepleted plasma in a single MRM-MS assay. Next, we aimed to demonstrate the potential of nondepleted plasma multiprotein-based models to discriminate psychiatric disorders using ML approaches, and to compare these with our previous models .…”

Differential Expression of Proteins Associated with Bipolar Disorder as Identified Using the PeptideShaker Software

“…For clinical practice, studies of biological fluids are more popular, according to which various physiological states of organs and systems, including the brain, are reflected in their composition [32,33]. Therefore, it is important to study potential biomarkers of mental disorders in an easily accessible material-blood serum [34][35][36][37][38][39][40][41]. However, all discovered proteins do not solve the problem of differential diagnosis of mental disorders due to their non-specificity.…”