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Xu F, Su Y, Wang X, Zhang T, Xie T, Wang Y. Olink proteomics analysis uncovers inflammatory proteins in patients with different states of bipolar disorder. Int Immunopharmacol 2024; 131:111816. [PMID: 38484669 DOI: 10.1016/j.intimp.2024.111816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/03/2024] [Accepted: 03/05/2024] [Indexed: 04/10/2024]
Abstract
STUDY DESIGN A prospective study. BACKGROUND This study aims to investigate the relationship between different states of bipolar disorder (BD) and plasma inflammatory proteins, which may be used as their biomarkers. MATERIALS AND METHODS We totally collected admission plasma from 16 healthy subjects and 32 BD patients, including 16 patients with BD manic episodes (BD-M) and 16 patients with BD depressive episodes (BD-D). Ten samples in each group were analyzed by proximity extension assays of 92 inflammation-related proteins, and all samples were verified by ELISA. Receiver-operating characteristic (ROC) curve analysis was performed to identify the diagnostic ability and cut-off values of potential biomarkers. RESULTS Our findings showed that BD patients had significantly higher levels of IL6, MCP-1, TGF-α, IL8, and IL10-RB in comparison with healthy subjects, and their cut-off values were 0.531 pg/ml, 0.531 pg/ml, 0.469 pg/ml, 0.406 pg/ml, and 0.406 pg/ml, respectively. The levels of IL6, MCP-1, TGF-α, and IL8 in BD-M patients were significantly greater than in healthy individuals, and their cut-off values were 0.813 pg/ml, 0.688 pg/ml, 0.438 pg/ml, and 0.625 pg/ml, respectively. Moreover, we found cut-off values of 0.500 pg/mL and 0.688 ng/mL for TGF-α and β-NGF, respectively, even though the levels in the BD-D group were much higher than in the control group. Furthermore, BD-M patients had significantly higher levels of IL6, FGF-19, IFN-γ, and IL-17C in comparison with BD-D patients. Likewise, 0.687 pg/ml, 0.500 pg/ml, 0.438 pg/ml, and 0.375 pg/ml were their cut-off values, respectively. Our findings also showed that the combination of these proteins had the highest diagnostic accuracy. CONCLUSIONS Our findings showed that plasma inflammatory proteins were related to BD and its subtypes, which may be utilized as potential biomarkers of different stages of BD. Furthermore, we also found their cut-off values and their combinations to have the highest diagnostic accuracy, providing clinicians with a new method to rapidly differentiate BD and its subtypes and manage early targeted interventions.
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Affiliation(s)
- Fangming Xu
- Mental Health Center, Hebei Medical University and Hebei Technical Innovation Center for Mental Health Assessment and Intervention, Shijiazhuang, Hebei Province 050031, China; Hebei Clinical Research Center for Mental Disorders and Institute of Mental Health, Shijiazhuang, Hebei Province 050031, China; Department of Psychiatry, the First Hospital of Hebei Medical University, Shijiazhuang, Hebei Province 050031, China; Hebei Key Laboratory of Brain Science and Psychiatric-Psychologic Disease, Shijiazhuang, Hebei Province 050031, China; Hebei Brain Ageing and Cognitive Neuroscience Laboratory, Shijiazhuang, Hebei Province 050031, China
| | - Yu Su
- Mental Health Center, Hebei Medical University and Hebei Technical Innovation Center for Mental Health Assessment and Intervention, Shijiazhuang, Hebei Province 050031, China; Hebei Clinical Research Center for Mental Disorders and Institute of Mental Health, Shijiazhuang, Hebei Province 050031, China; Department of Psychiatry, the First Hospital of Hebei Medical University, Shijiazhuang, Hebei Province 050031, China; Hebei Key Laboratory of Brain Science and Psychiatric-Psychologic Disease, Shijiazhuang, Hebei Province 050031, China; Hebei Brain Ageing and Cognitive Neuroscience Laboratory, Shijiazhuang, Hebei Province 050031, China
| | - Xiaobo Wang
- Mental Health Center, Hebei Medical University and Hebei Technical Innovation Center for Mental Health Assessment and Intervention, Shijiazhuang, Hebei Province 050031, China; Hebei Clinical Research Center for Mental Disorders and Institute of Mental Health, Shijiazhuang, Hebei Province 050031, China; Department of Psychiatry, the First Hospital of Hebei Medical University, Shijiazhuang, Hebei Province 050031, China; Hebei Key Laboratory of Brain Science and Psychiatric-Psychologic Disease, Shijiazhuang, Hebei Province 050031, China; Hebei Brain Ageing and Cognitive Neuroscience Laboratory, Shijiazhuang, Hebei Province 050031, China
| | - Tianle Zhang
- Hebei Medical University, Shijiazhuang, Hebei Province 050031, China
| | - Tingting Xie
- Mental Health Center, Hebei Medical University and Hebei Technical Innovation Center for Mental Health Assessment and Intervention, Shijiazhuang, Hebei Province 050031, China; Hebei Clinical Research Center for Mental Disorders and Institute of Mental Health, Shijiazhuang, Hebei Province 050031, China; Department of Psychiatry, the First Hospital of Hebei Medical University, Shijiazhuang, Hebei Province 050031, China; Hebei Key Laboratory of Brain Science and Psychiatric-Psychologic Disease, Shijiazhuang, Hebei Province 050031, China; Hebei Brain Ageing and Cognitive Neuroscience Laboratory, Shijiazhuang, Hebei Province 050031, China
| | - Yumei Wang
- Mental Health Center, Hebei Medical University and Hebei Technical Innovation Center for Mental Health Assessment and Intervention, Shijiazhuang, Hebei Province 050031, China; Hebei Clinical Research Center for Mental Disorders and Institute of Mental Health, Shijiazhuang, Hebei Province 050031, China; Department of Psychiatry, the First Hospital of Hebei Medical University, Shijiazhuang, Hebei Province 050031, China; Hebei Key Laboratory of Brain Science and Psychiatric-Psychologic Disease, Shijiazhuang, Hebei Province 050031, China; Hebei Brain Ageing and Cognitive Neuroscience Laboratory, Shijiazhuang, Hebei Province 050031, China; Department of Psychology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan 250021, Shandong, China.
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Yin B, Cai Y, Teng T, Wang X, Liu X, Li X, Wang J, Wu H, He Y, Ren F, Kou T, Zhu ZJ, Zhou X. Identifying plasma metabolic characteristics of major depressive disorder, bipolar disorder, and schizophrenia in adolescents. Transl Psychiatry 2024; 14:163. [PMID: 38531835 DOI: 10.1038/s41398-024-02886-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 03/14/2024] [Accepted: 03/19/2024] [Indexed: 03/28/2024] Open
Abstract
Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are classified as major mental disorders and together account for the second-highest global disease burden, and half of these patients experience symptom onset in adolescence. Several studies have reported both similar and unique features regarding the risk factors and clinical symptoms of these three disorders. However, it is still unclear whether these disorders have similar or unique metabolic characteristics in adolescents. We conducted a metabolomics analysis of plasma samples from adolescent healthy controls (HCs) and patients with MDD, BD, and SCZ. We identified differentially expressed metabolites between patients and HCs. Based on the differentially expressed metabolites, correlation analysis, metabolic pathway analysis, and potential diagnostic biomarker identification were conducted for disorders and HCs. Our results showed significant changes in plasma metabolism between patients with these mental disorders and HCs; the most distinct changes were observed in SCZ patients. Moreover, the metabolic differences in BD patients shared features with those in both MDD and SCZ, although the BD metabolic profile was closer to that of MDD than to SCZ. Additionally, we identified the metabolites responsible for the similar and unique metabolic characteristics in multiple metabolic pathways. The similar significant differences among the three disorders were found in fatty acid, steroid-hormone, purine, nicotinate, glutamate, tryptophan, arginine, and proline metabolism. Interestingly, we found unique characteristics of significantly altered glycolysis, glycerophospholipid, and sphingolipid metabolism in SCZ; lysine, cysteine, and methionine metabolism in MDD and BD; and phenylalanine, tyrosine, and aspartate metabolism in SCZ and BD. Finally, we identified five panels of potential diagnostic biomarkers for MDD-HC, BD-HC, SCZ-HC, MDD-SCZ, and BD-SCZ comparisons. Our findings suggest that metabolic characteristics in plasma vary across psychiatric disorders and that critical metabolites provide new clues regarding molecular mechanisms in these three psychiatric disorders.
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Affiliation(s)
- Bangmin Yin
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuping Cai
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Teng Teng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaolin Wang
- Health Management Center, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xueer Liu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xuemei Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jie Wang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongyan Wu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yuqian He
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fandong Ren
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Tianzhang Kou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
- Shanghai Key Laboratory of Aging Studies, Shanghai, China.
| | - Xinyu Zhou
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Shin D, Lee J, Kim Y, Park J, Shin D, Song Y, Joo EJ, Roh S, Lee KY, Oh S, Ahn YM, Rhee SJ, Kim Y. Evaluation of a Nondepleted Plasma Multiprotein-Based Model for Discriminating Psychiatric Disorders Using Multiple Reaction Monitoring-Mass Spectrometry: Proof-of-Concept Study. J Proteome Res 2024; 23:329-343. [PMID: 38063806 DOI: 10.1021/acs.jproteome.3c00580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
Psychiatric evaluation relies on subjective symptoms and behavioral observation, which sometimes leads to misdiagnosis. Despite previous efforts to utilize plasma proteins as objective markers, the depletion method is time-consuming. Therefore, this study aimed to enhance previous quantification methods and construct objective discriminative models for major psychiatric disorders using nondepleted plasma. Multiple reaction monitoring-mass spectrometry (MRM-MS) assays for quantifying 453 peptides in nondepleted plasma from 132 individuals [35 major depressive disorder (MDD), 47 bipolar disorder (BD), 23 schizophrenia (SCZ) patients, and 27 healthy controls (HC)] were developed. Pairwise discriminative models for MDD, BD, and SCZ, and a discriminative model between patients and HC were constructed by machine learning approaches. In addition, the proteins from nondepleted plasma-based discriminative models were compared with previously developed depleted plasma-based discriminative models. Discriminative models for MDD versus BD, BD versus SCZ, MDD versus SCZ, and patients versus HC were constructed with 11 to 13 proteins and showed reasonable performances (AUROC = 0.890-0.955). Most of the shared proteins between nondepleted and depleted plasma models had consistent directions of expression levels and were associated with neural signaling, inflammatory, and lipid metabolism pathways. These results suggest that multiprotein markers from nondepleted plasma have a potential role in psychiatric evaluation.
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Affiliation(s)
- Dongyoon Shin
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam 13520, Republic of Korea
| | - Jihyeon Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Yeongshin Kim
- Department of Life Science, General Graduate School, CHA University, Seongnam 13488, Republic of Korea
| | - Junho Park
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam 13520, Republic of Korea
- Department of Life Science, General Graduate School, CHA University, Seongnam 13488, Republic of Korea
| | - Daun Shin
- Department of Psychiatry, Korea University Anam Hospital, Seoul 02841, Republic of Korea
| | - Yoojin Song
- Department of Psychiatry, Kangwon National University Hospital, Chuncheon 24289, Republic of Korea
| | - Eun-Jeong Joo
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon 34824, Republic of Korea
- Department of Psychiatry, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu 11759, Republic of Korea
| | - Sungwon Roh
- Department of Psychiatry, Hanyang University Hospital and Hanyang University College of Medicine, Seoul 04763, Republic of Korea
| | - Kyu Young Lee
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon 34824, Republic of Korea
- Department of Psychiatry, Nowon Eulji University Hospital, Seoul 01830, Republic of Korea
| | - Sanghoon Oh
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon 34824, Republic of Korea
- Department of Psychiatry, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu 11759, Republic of Korea
| | - Yong Min Ahn
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul 03080, Republic of Korea
| | - Sang Jin Rhee
- Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Republic of Korea
| | - Youngsoo Kim
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam 13520, Republic of Korea
- Department of Life Science, General Graduate School, CHA University, Seongnam 13488, Republic of Korea
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Rhee SJ, Shin D, Shin D, Song Y, Joo EJ, Jung HY, Roh S, Lee SH, Kim H, Bang M, Lee KY, Lee J, Kim J, Kim Y, Kim Y, Ahn YM. Network analysis of plasma proteomes in affective disorders. Transl Psychiatry 2023; 13:195. [PMID: 37296094 DOI: 10.1038/s41398-023-02485-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 05/13/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
The conventional differentiation of affective disorders into major depressive disorder (MDD) and bipolar disorder (BD) has insufficient biological evidence. Utilizing multiple proteins quantified in plasma may provide critical insight into these limitations. In this study, the plasma proteomes of 299 patients with MDD or BD (aged 19-65 years old) were quantified using multiple reaction monitoring. Based on 420 protein expression levels, a weighted correlation network analysis was performed. Significant clinical traits with protein modules were determined using correlation analysis. Top hub proteins were determined using intermodular connectivity, and significant functional pathways were identified. Weighted correlation network analysis revealed six protein modules. The eigenprotein of a protein module with 68 proteins, including complement components as hub proteins, was associated with the total Childhood Trauma Questionnaire score (r = -0.15, p = 0.009). Another eigenprotein of a protein module of 100 proteins, including apolipoproteins as hub proteins, was associated with the overeating item of the Symptom Checklist-90-Revised (r = 0.16, p = 0.006). Functional analysis revealed immune responses and lipid metabolism as significant pathways for each module, respectively. No significant protein module was associated with the differentiation between MDD and BD. In conclusion, childhood trauma and overeating symptoms were significantly associated with plasma protein networks and should be considered important endophenotypes in affective disorders.
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Affiliation(s)
- Sang Jin Rhee
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Dongyoon Shin
- Department of Biomedical Science, School of Medicine, CHA University, Seongnam, Republic of Korea
| | - Daun Shin
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yoojin Song
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Eun-Jeong Joo
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon, Republic of Korea
- Department of Psychiatry, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu, Republic of Korea
| | - Hee Yeon Jung
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Sungwon Roh
- Department of Psychiatry, Hanyang University Hospital and Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Sang-Hyuk Lee
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Hyeyoung Kim
- Department of Psychiatry, Inha University Hospital, Incheon, Republic of Korea
| | - Minji Bang
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam, Republic of Korea
| | - Kyu Young Lee
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon, Republic of Korea
- Department of Psychiatry, Nowon Eulji University Hospital, Seoul, Republic of Korea
| | - Jihyeon Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jaenyeon Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yeongshin Kim
- Department of Biomedical Science, School of Medicine, CHA University, Seongnam, Republic of Korea
| | - Youngsoo Kim
- Department of Biomedical Science, School of Medicine, CHA University, Seongnam, Republic of Korea.
| | - Yong Min Ahn
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
- Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
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Latent class analysis of psychotic-affective disorders with data-driven plasma proteomics. Transl Psychiatry 2023; 13:44. [PMID: 36746927 PMCID: PMC9902608 DOI: 10.1038/s41398-023-02321-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 01/09/2023] [Accepted: 01/13/2023] [Indexed: 02/08/2023] Open
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.
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