Integrating proteomic and clinical data to discriminate major psychiatric disorders: Applications for major depressive disorder, bipolar disorder, and schizophrenia

Shin, Dongyoon; Rhee, Sang Jin; Shin, Daun; Joo, Eun Jeong; Jung, Hee Yeon; Roh, Sungwon; Lee, Sanghyuk; Kim, Hye Young; Bang, Minji; Lee, Kyu Young; Kim, Se Hyun; Lee, Jihyeon; Kim, Yoseop; Yeo, In Wook; Kim, Yeongshin; Kim, Jaenyeon; Kwon, Jun Soo; Ha, Kyooseob; Ahn, Yong Min; Kim, Youngsoo

doi:10.1002/ctm2.929

Cited by 8 publications

(29 citation statements)

References 12 publications

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“…Current psychotropic medication use was not an exclusion criterion. Most studies were based on previous reports on the association between these conditions and altered protein expression [ 7 ]. Those who had recently received neuromodulation and psychotherapy were excluded to confine the treatment effects to psychotropic medications.…”

Section: Methodsmentioning

confidence: 99%

“…More specific methods for targeted proteomic analysis have been described previously [ 7 ], and are described in Supplementary methods . For each plasma sample, the six most abundant proteins were depleted using a MARS-6 column (Agilent Technologies, Santa Clara, CA, USA).…”

Section: Methodsmentioning

confidence: 99%

“…As from our previous study, 588 stable peptide markers were found to be eligible as proteomic candidates [ 7 ]. Log2 transformation followed by batch correction for sample preparation batches using the Combat algorithm was performed using the R package proBatch [ 20 ].…”

Section: Methodsmentioning

confidence: 99%

“…Log2 transformation followed by batch correction for sample preparation batches using the Combat algorithm was performed using the R package proBatch [ 20 ]. Peptides with PAR values ≤0.01 or ≥100 for at least 5% of the total study population were initially excluded, in line with our previous study [ 7 ]. First, peptide markers were chosen when they were statistically significant in univariate analysis with the peptide values as the dependent variable and patients versus HC as the dichotomous independent variable.…”

Section: Methodsmentioning

confidence: 99%

“…However, to the best of our knowledge, no study has focused on proteomics-based cluster analysis in a transdiagnostic psychotic-affective disorder spectrum to date. This study was based on a previous study in which we differentiated SCZ, BD, and MDD using plasma proteins [ 7 ]. In this study, we aimed to identify clusters based on peripheral plasma proteomic data.…”

Section: Introductionmentioning

confidence: 99%

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

Rhee

Shin

et al. 2023

Transl Psychiatry

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%