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Lengvenyte A, Cognasse F, Hamzeh-Cognasse H, Sénèque M, Strumila R, Olié E, Courtet P. Baseline circulating biomarkers, their changes, and subsequent suicidal ideation and depression severity at 6 months: A prospective analysis in patients with mood disorders. Psychoneuroendocrinology 2024; 168:107119. [PMID: 39003840 DOI: 10.1016/j.psyneuen.2024.107119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 06/19/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND Identifying circulating biomarkers associated with prospective suicidal ideation (SI) and depression could help better understand the dynamics of these phenomena and identify people in need of intense care. In this study, we investigated the associations between baseline peripheral biomarkers implicated in neuroplasticity, vascular homeostasis and inflammation, and prospective SI and depression severity during 6 months of follow-up in patients with mood disorders. METHODS 149 patients underwent a psychiatric evaluation and gave blood to measure 32 plasma soluble proteins. At follow-up, SI incidence over six months was measured with the Columbia Suicide Severity Rating Scale, and depressive symptoms were assessed with the Inventory for Depressive Symptomatology. Ninety-six patients provided repeated blood samples. Statistical analyses included Spearman partial correlation and Elastic Net regression, followed by the covariate-adjusted regression models. RESULTS 51.4 % (N = 71) of patients reported SI during follow-up. After adjustment for covariates, higher baseline levels of interferon-γ were associated with SI occurrence during follow-up. Higher baseline interferon-γ and lower orexin-A were associated with increased depression severity, and atypical and anxious, but not melancholic, symptoms. There was also a tendency for associations of elevated baseline levels of interferon-γ, interleukin-1β, and lower plasma serotonin levels with SI at the six-month follow-up time point. Meanwhile, reduction in transforming growth factor- β1 (TGF-β1) plasma concentration correlated with atypical symptoms reduction. CONCLUSION We identified interferon-γ and orexin-A as potential predictive biomarkers of SI and depression, whereas TGF-β1 was identified as a possible target of atypical symptoms.
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Affiliation(s)
- Aiste Lengvenyte
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France; Faculty of Medicine, Institute of Clinical Medicine, Psychiatric Clinic, Vilnius University, Vilnius, Lithuania.
| | - Fabrice Cognasse
- Université Jean Monnet, Mines Saint-Étienne, INSERM, U 1059 Sainbiose, Saint-Étienne, France; Etablissement Français du Sang Auvergne-Rhône-Alpes, Saint-Étienne, France
| | - Hind Hamzeh-Cognasse
- Université Jean Monnet, Mines Saint-Étienne, INSERM, U 1059 Sainbiose, Saint-Étienne, France
| | - Maude Sénèque
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Robertas Strumila
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France; Faculty of Medicine, Institute of Clinical Medicine, Psychiatric Clinic, Vilnius University, Vilnius, Lithuania
| | - Emilie Olié
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Philippe Courtet
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France
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Lyu N, Wang H, Zhao Q, Fu B, Li J, Yue Z, Huang J, Yang F, Liu H, Zhang L, Li R. Peripheral biomarkers to differentiate bipolar depression from major depressive disorder: a real-world retrospective study. BMC Psychiatry 2024; 24:543. [PMID: 39085797 PMCID: PMC11293032 DOI: 10.1186/s12888-024-05979-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 07/22/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Bipolar depression (BPD) is often misdiagnosed as a major depressive disorder (MDD) in clinical practice, which may be attributed to a lack of robust biomarkers indicative of differentiated diagnosis. This study analysed the differences in various hormones and inflammatory markers to explore peripheral biomarkers that differentiate BPD from MDD patients. METHODS A total of 2,048 BPD and MDD patients were included. A panel of blood tests was performed to determine the levels of sex hormones, stress hormones, and immune-related indicators. Propensity score matching (PSM) was used to control for the effect of potential confounders between two groups and further a receiver operating characteristic (ROC) curve was used to analyse the potential biomarkers for differentiating BPD from MDD. RESULTS Compared to patients with MDD, patients with BPD expressed a longer duration of illness, more hospitalisations within five years, and an earlier age of onset, along with fewer comorbid psychotic symptoms. In terms of biochemical parameters, MDD patients presented higher IgA and IgM levels, while BPD patients featured more elevated neutrophil and monocyte counts. ROC analysis suggested that combined biological indicators and clinical features could moderately distinguish between BPD and MDD. In addition, different biological features exist in BPD and MDD patients of different ages and sexes. CONCLUSIONS Differential peripheral biological parameters were observed between BPD and MDD, which may be age-sex specific, and a combined diagnostic model that integrates clinical characteristics and biochemical indicators has a moderate accuracy in distinguishing BPD from MDD.
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Affiliation(s)
- Nan Lyu
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- Laboratory for Clinical Medicine, Capital Medical University, Beijing, China
| | - Han Wang
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- Laboratory for Clinical Medicine, Capital Medical University, Beijing, China
| | - Qian Zhao
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- Laboratory for Clinical Medicine, Capital Medical University, Beijing, China
| | - Bingbing Fu
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
| | - Jinhong Li
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
| | - Ziqi Yue
- National Center for Cardiovascular Diseases and Fuwai Hospital, Beijing, China
| | - Juan Huang
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
| | - Fan Yang
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
| | - Hao Liu
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China
| | - Ling Zhang
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China.
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Rena Li
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, 5 Ankang Hutong Road, Xicheng District, Beijing, 100088, China.
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
- Center for Brain Disorders Research, Capital Medical University & Beijing Institute of Brain Disorders, Beijing, China.
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Mews P, Sosnick L, Gurung A, Sidoli S, Nestler EJ. Decoding cocaine-induced proteomic adaptations in the mouse nucleus accumbens. Sci Signal 2024; 17:eadl4738. [PMID: 38626009 PMCID: PMC11170322 DOI: 10.1126/scisignal.adl4738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 03/28/2024] [Indexed: 04/18/2024]
Abstract
Cocaine use disorder (CUD) is a chronic neuropsychiatric condition that results from enduring cellular and molecular adaptations. Among substance use disorders, CUD is notable for its rising prevalence and the lack of approved pharmacotherapies. The nucleus accumbens (NAc), a region that is integral to the brain's reward circuitry, plays a crucial role in the initiation and continuation of maladaptive behaviors that are intrinsic to CUD. Leveraging advancements in neuroproteomics, we undertook a proteomic analysis that spanned membrane, cytosolic, nuclear, and chromatin compartments of the NAc in a mouse model. The results unveiled immediate and sustained proteomic modifications after cocaine exposure and during prolonged withdrawal. We identified congruent protein regulatory patterns during initial cocaine exposure and reexposure after withdrawal, which contrasted with distinct patterns during withdrawal. Pronounced proteomic shifts within the membrane compartment indicated adaptive and long-lasting molecular responses prompted by cocaine withdrawal. In addition, we identified potential protein translocation events between soluble-nuclear and chromatin-bound compartments, thus providing insight into intracellular protein dynamics after cocaine exposure. Together, our findings illuminate the intricate proteomic landscape that is altered in the NAc by cocaine use and provide a dataset for future research toward potential therapeutics.
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Affiliation(s)
- Philipp Mews
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lucas Sosnick
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ashik Gurung
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Simone Sidoli
- Department of Biochemistry, Albert Einstein College of Medicine, New York, NY 10461, USA
| | - Eric J. Nestler
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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Lengvenyte A, Belzeaux R, Olié E, Hamzeh-Cognasse H, Sénèque M, Strumila R, Cognasse F, Courtet P. Associations of potential plasma biomarkers with suicide attempt history, current suicidal ideation and subsequent suicidal events in patients with depression: A discovery study. Brain Behav Immun 2023; 114:242-254. [PMID: 37648005 DOI: 10.1016/j.bbi.2023.08.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 08/22/2023] [Accepted: 08/26/2023] [Indexed: 09/01/2023] Open
Abstract
A growing body of evidences suggests that suicidal ideation (SI) and suicidal behaviors have biological bases. However, no biological marker is currently available to evaluate the suicide risk in individuals with SI or suicide attempt (SA). Moreover, the current risk assessment techniques poorly predict future suicidal events. The aim of this study was to examine the association of 39 new and already described peripheral cells and proteins (implicated in the immune system, oxidative stress and plasticity) with lifetime SA, past month SA, current SI, and future suicidal events (visit to the Emergency Department for SI or SA) in 266 treatment-seeking individuals with mood disorders. Equal parts of patients with and without past history of SA were recruited. All individuals at inclusion gave blood, were evaluated for SA recency, current SI, and were followed for two years afterwards. The 39 peripheral blood cellular and protein markers were entered separately for each outcome in Elastic Net models with 10-fold cross-validation, followed by single-analyte covariate-adjusted regression analyses for pre-selected analytes. Past month SA was associated with increased plasma levels of thrombospondin-2 and C-reactive protein, whereas current SI was associated with lower plasma serotonin levels. These associations were robust to adjustments for key covariates and corrections for multiple testing. The Cox proportional hazards regression showed that higher levels of thrombospondin-1 and of platelet-derived growth factor-AB predicted a future suicidal event. These two associations remained after adjustment for sex, age, and SA history, and outperformed the predictive value of past SA. Thrombospondins and platelet-derived growth factors have never been investigated in the context of suicide. Altogether, our results highlight the involvement in the suicidal process of platelet biological response and plasticity modifiers and also of inflammatory factors. They also suggest that SI and SA may have different biological correlates and that biomarkers associated with past SA or current SI do not automatically also predict future events.
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Affiliation(s)
- Aiste Lengvenyte
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France; Faculty of Medicine, Institute of Clinical Medicine, Psychiatric Clinic, Vilnius University, Vilnius, Lithuania.
| | - Raoul Belzeaux
- INT-UMR7289, CNRS Aix-Marseille Université, Marseille, France; University Department of Adult Psychiatry, CHU Montpellier, Montpellier, France; Fondation Fondamental
| | - Emilie Olié
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France; Fondation Fondamental
| | - Hind Hamzeh-Cognasse
- Université Jean Monnet, Mines Saint-Étienne, INSERM, U 1059 Sainbiose, Saint-Étienne, France
| | - Maude Sénèque
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Robertas Strumila
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France; Faculty of Medicine, Institute of Clinical Medicine, Psychiatric Clinic, Vilnius University, Vilnius, Lithuania
| | - Fabrice Cognasse
- Université Jean Monnet, Mines Saint-Étienne, INSERM, U 1059 Sainbiose, Saint-Étienne, France; Etablissement Français du Sang Auvergne-Rhône-Alpes, Saint-Étienne, France
| | - Philippe Courtet
- Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France; IGF, University of Montpellier, CNRS, INSERM, Montpellier, France; Fondation Fondamental
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de Jesus JR, de Araujo Andrade T, de Figueiredo EC. Biomarkers in psychiatric disorders. Adv Clin Chem 2023; 116:183-208. [PMID: 37852719 DOI: 10.1016/bs.acc.2023.05.005] [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: 10/20/2023]
Abstract
Psychiatric disorders represent a significant socioeconomic and healthcare burden worldwide. Of these, schizophrenia, bipolar disorder, major depressive disorder and anxiety are among the most prevalent. Unfortunately, diagnosis remains problematic and largely complicated by the lack of disease specific biomarkers. Accordingly, much research has focused on elucidating these conditions to more fully understand underlying pathophysiology and potentially identify biomarkers, especially those of early stage disease. In this chapter, we review current status of this endeavor as well as the potential development of novel biomarkers for clinical applications and future research study.
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Affiliation(s)
| | | | - Eduardo Costa de Figueiredo
- Faculty of Pharmaceutical Sciences, Federal University of Alfenas, Rua Gabriel Monteiro da Silva, Alfenas, Minas Gerais, Brazil
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Ribeiro HC, Zandonadi FDS, Sussulini A. An overview of metabolomic and proteomic profiling in bipolar disorder and its clinical value. Expert Rev Proteomics 2023; 20:267-280. [PMID: 37830362 DOI: 10.1080/14789450.2023.2267756] [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: 07/03/2023] [Accepted: 09/12/2023] [Indexed: 10/14/2023]
Abstract
INTRODUCTION Bipolar disorder (BD) is a complex psychiatric disease characterized by alternating mood episodes. As for any other psychiatric illness, currently there is no biochemical test that is able to support diagnosis or therapeutic decisions for BD. In this context, the discovery and validation of biomarkers are interesting strategies that can be achieved through proteomics and metabolomics. AREAS COVERED In this descriptive review, a literature search including original articles and systematic reviews published in the last decade was performed with the objective to discuss the results of BD proteomic and metabolomic profiling analyses and indicate proteins and metabolites (or metabolic pathways) with potential clinical value. EXPERT OPINION A large number of proteins and metabolites have been reported as potential BD biomarkers; however, most studies do not reach biomarker validation stages. An effort from the scientific community should be directed toward the validation of biomarkers and the development of simplified bioanalytical techniques or protocols to determine them in biological samples, in order to translate proteomic and metabolomic findings into clinical routine assays.
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Affiliation(s)
- Henrique Caracho Ribeiro
- Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Department of Analytical Chemistry, Institute of Chemistry, Universidade Estadual de Campinas(UNICAMP), Campinas, SP, Brazil
| | - Flávia da Silva Zandonadi
- Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Department of Analytical Chemistry, Institute of Chemistry, Universidade Estadual de Campinas(UNICAMP), Campinas, SP, Brazil
| | - Alessandra Sussulini
- Laboratory of Bioanalytics and Integrated Omics (LaBIOmics), Department of Analytical Chemistry, Institute of Chemistry, Universidade Estadual de Campinas(UNICAMP), Campinas, SP, Brazil
- Instituto Nacional de Ciência e Tecnologia de Bioanalítica (INCTBio), Institute of Chemistry, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil
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Waszczuk MA, Kuan PF, Yang X, Miao J, Kotov R, Luft BJ. Discovery and replication of blood-based proteomic signature of PTSD in 9/11 responders. Transl Psychiatry 2023; 13:8. [PMID: 36631443 PMCID: PMC9834302 DOI: 10.1038/s41398-022-02302-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 11/28/2022] [Accepted: 12/22/2022] [Indexed: 01/13/2023] Open
Abstract
Proteomics provides an opportunity to develop biomarkers for the early detection and monitoring of post-traumatic stress disorder (PTSD). However, research to date has been limited by small sample sizes and a lack of replication. This study performed Olink Proseek Multiplex Platform profiling of 81 proteins involved in neurological processes in 936 responders to the 9/11 disaster (mean age at blood draw = 55.41 years (SD = 7.93), 94.1% white, all men). Bivariate correlations and elastic net regressions were used in a discovery subsample to identify concurrent associations between PTSD symptom severity and the profiled proteins, and to create a multiprotein composite score. In hold-out subsamples, nine bivariate associations between PTSD symptoms and differentially expressed proteins were replicated: SKR3, NCAN, BCAN, MSR1, PVR, TNFRSF21, DRAXIN, CLM6, and SCARB2 (|r| = 0.08-0.17, p < 0.05). There were three replicated bivariate associations between lifetime PTSD diagnosis and differentially expressed proteins: SKR3, SIGLEC, and CPM (OR = 1.38-1.50, p < 0.05). The multiprotein composite score retained 38 proteins, including 10/11 proteins that replicated in bivariate tests. The composite score was significantly associated with PTSD symptom severity (β = 0.27, p < 0.001) and PTSD diagnosis (OR = 1.60, 95% CI: 1.17-2.19, p = 0.003) in the hold-out subsample. Overall, these findings suggest that PTSD is characterized by altered expression of several proteins implicated in neurological processes. Replicated associations with TNFRSF21, CLM6, and PVR support the neuroinflammatory signature of PTSD. The multiprotein composite score substantially increased associations with PTSD symptom severity over individual proteins. If generalizable to other populations, the current findings may inform the development of PTSD biomarkers.
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Affiliation(s)
- Monika A Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA
| | - Pei-Fen Kuan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Xiaohua Yang
- Department of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Jiaju Miao
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Roman Kotov
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA
| | - Benjamin J Luft
- Department of Medicine, Stony Brook University, Stony Brook, NY, USA.
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Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis. Neurosci Biobehav Rev 2022; 135:104552. [PMID: 35120970 DOI: 10.1016/j.neubiorev.2022.104552] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/11/2022] [Accepted: 01/30/2022] [Indexed: 01/10/2023]
Abstract
Applying machine learning (ML) to objective markers may overcome prognosis uncertainty due to the subjective nature of the diagnosis of bipolar disorder (BD). This PRISMA-compliant meta-analysis provides new systematic evidence of the BD classification accuracy reached by different markers and ML algorithms. We focused on neuroimaging, electrophysiological techniques, peripheral biomarkers, genetic data, neuropsychological or clinical measures, and multimodal approaches. PubMed, Embase and Scopus were searched through 3rd December 2020. Meta-analyses were performed using random-effect models. Overall, 81 studies were included in this systematic review and 65 in the meta-analysis (11,336 participants, 3,903 BD). The overall pooled classification accuracy was 0.77 (95%CI[0.75;0.80]). Despite subgroup analyses for diagnostic comparison group, psychiatric disorders, marker, ML algorithm, and validation procedure were not significant, linear discriminant analysis significantly outperformed support vector machine for peripheral biomarkers (p=0.03). Sample size was inversely related to accuracy. Evidence of publication bias was detected. Ultimately, although ML reached a high accuracy in differentiating BD from other psychiatric disorders, best practices in methodology are needed for the advancement of future studies.
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Shin D, Rhee SJ, Lee J, Yeo I, Do M, Joo EJ, Jung HY, Roh S, Lee SH, Kim H, Bang M, Lee KY, Kwon JS, Ha K, Ahn YM, Kim Y. Quantitative Proteomic Approach for Discriminating Major Depressive Disorder and Bipolar Disorder by Multiple Reaction Monitoring-Mass Spectrometry. J Proteome Res 2021; 20:3188-3203. [PMID: 33960196 DOI: 10.1021/acs.jproteome.1c00058] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.
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Affiliation(s)
| | - Sang Jin Rhee
- 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
| | | | | | | | - Eun-Jeong Joo
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon 34824, Republic of Korea.,Department of Psychiatry, Nowon Eulji Medical Center, Eulji University, Seoul 01830, Republic of Korea
| | - Hee Yeon Jung
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul 07061, Republic of Korea.,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, 101 Daehakro, Seoul 30380, Republic of Korea
| | - Sungwon Roh
- Department of Psychiatry, Hanyang University Hospital, Seoul 04763, Republic of Korea.,Department of Psychiatry, Hanyang University College of Medicine, Seoul 04763, Republic of Korea
| | - Sang-Hyuk Lee
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam 13496, Republic of Korea
| | - Hyeyoung Kim
- Department of Psychiatry, Inha University Hospital, Incheon 22332, Republic of Korea
| | - Minji Bang
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam 13496, Republic of Korea
| | - Kyu Young Lee
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon 34824, Republic of Korea.,Department of Psychiatry, Nowon Eulji Medical Center, Eulji University, Seoul 01830, Republic of Korea
| | - Jun Soo Kwon
- 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, 101 Daehakro, Seoul 30380, Republic of Korea
| | - Kyooseob Ha
- 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, 101 Daehakro, Seoul 30380, 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, 101 Daehakro, Seoul 30380, Republic of Korea
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Tomasik J, Han SYS, Barton-Owen G, Mirea DM, Martin-Key NA, Rustogi N, Lago SG, Olmert T, Cooper JD, Ozcan S, Eljasz P, Thomas G, Tuytten R, Metcalfe T, Schei TS, Farrag LP, Friend LV, Bell E, Cowell D, Bahn S. A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data. Transl Psychiatry 2021; 11:41. [PMID: 33436544 PMCID: PMC7804187 DOI: 10.1038/s41398-020-01181-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 12/14/2020] [Accepted: 12/15/2020] [Indexed: 12/17/2022] Open
Abstract
The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.
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Affiliation(s)
- Jakub Tomasik
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
| | - Sung Yeon Sarah Han
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | | | - Dan-Mircea Mirea
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA
| | - Nayra A Martin-Key
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Nitin Rustogi
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Santiago G Lago
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Tony Olmert
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- University of California San Diego School of Medicine, San Diego, California, USA
| | - Jason D Cooper
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- Owlstone Medical Ltd, Cambridge, UK
| | - Sureyya Ozcan
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
- Department of Chemistry, Middle East Technical University, Ankara, Turkey
| | - Pawel Eljasz
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | | | - Robin Tuytten
- Metabolomic Diagnostics, Little Island, Cork, Ireland
| | | | | | | | | | | | | | - Sabine Bahn
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
- Psyomics Ltd, Cambridge, UK.
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Rappeneau V, Wilmes L, Touma C. Molecular correlates of mitochondrial dysfunctions in major depression: Evidence from clinical and rodent studies. Mol Cell Neurosci 2020; 109:103555. [PMID: 32979495 DOI: 10.1016/j.mcn.2020.103555] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 08/24/2020] [Accepted: 09/03/2020] [Indexed: 12/13/2022] Open
Abstract
Major depressive disorder (MDD) is one of the most prevalent stress-related mental disorders worldwide. Several biological mechanisms underlying the pathophysiology of MDD have been proposed, including endocrine disturbances, neurotransmitter deficits, impaired neuronal plasticity, and more recently, mitochondrial dysfunctions. In this review, we provide an overview of relevant molecular correlates of mitochondrial dysfunction in MDD, based on findings from clinical studies and stress-induced rodent models. We also compare differences and similarities between the phenotypes of MDD patients and animal models. Our analysis of the literature reveals that both MDD and stress are associated, in humans and animals, with changes in mitochondrial biogenesis, redox imbalance, increased oxidative damages of cellular macromolecules, and apoptosis. Yet, a considerable amount of conflicting data exist and therefore, the translation of findings from clinical and preclinical research to novel therapies for MDD remains complex. Further studies are needed to advance our understanding of the molecular networks and biological mechanisms involving mitochondria in the pathophysiology of MDD.
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Affiliation(s)
- Virginie Rappeneau
- Department of Behavioural Biology, University of Osnabrück, Osnabrück, Germany.
| | - Lars Wilmes
- Department of Behavioural Biology, University of Osnabrück, Osnabrück, Germany
| | - Chadi Touma
- Department of Behavioural Biology, University of Osnabrück, Osnabrück, Germany
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