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Cruz-Gonzalez P, He AWJ, Lam EP, Ng IMC, Li MW, Hou R, Chan JNM, Sahni Y, Vinas Guasch N, Miller T, Lau BWM, Sánchez Vidaña DI. Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications. Psychol Med 2025; 55:e18. [PMID: 39911020 PMCID: PMC12017374 DOI: 10.1017/s0033291724003295] [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: 04/15/2024] [Revised: 10/26/2024] [Accepted: 11/26/2024] [Indexed: 02/07/2025]
Abstract
Artificial intelligence (AI) has been recently applied to different mental health illnesses and healthcare domains. This systematic review presents the application of AI in mental health in the domains of diagnosis, monitoring, and intervention. A database search (CCTR, CINAHL, PsycINFO, PubMed, and Scopus) was conducted from inception to February 2024, and a total of 85 relevant studies were included according to preestablished inclusion criteria. The AI methods most frequently used were support vector machine and random forest for diagnosis, machine learning for monitoring, and AI chatbot for intervention. AI tools appeared to be accurate in detecting, classifying, and predicting the risk of mental health conditions as well as predicting treatment response and monitoring the ongoing prognosis of mental health disorders. Future directions should focus on developing more diverse and robust datasets and on enhancing the transparency and interpretability of AI models to improve clinical practice.
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Affiliation(s)
- Pablo Cruz-Gonzalez
- Rehabilitation Research Institute of Singapore, Nanyang Technological University, Singapore, Singapore
| | - Aaron Wan-Jia He
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Elly PoPo Lam
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Ingrid Man Ching Ng
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Mandy Wingman Li
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Rangchun Hou
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Jackie Ngai-Man Chan
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Yuvraj Sahni
- Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Nestor Vinas Guasch
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Tiev Miller
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Benson Wui-Man Lau
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
- Mental Health Research Center, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
| | - Dalinda Isabel Sánchez Vidaña
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
- Mental Health Research Center, The Hong Kong Polytechnic University, Hong Kong, Hong Kong
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Zhang L, Swaab DF. Neuroglia in suicide. HANDBOOK OF CLINICAL NEUROLOGY 2025; 210:371-379. [PMID: 40148056 DOI: 10.1016/b978-0-443-19102-2.00018-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2025]
Abstract
Suicide is the worst outcome for many neuropsychiatric disorders having a high social and economic burden. There is a great need to determine the neurobiologic background of the etiopathogenesis and resilience toward suicide and to find novel pharmacologic strategies to treat suicidal behaviors. Neuroglia have been found to actively participate in the regulation of many cerebral functions, but it is debated whether these cells are structurally or functionally involved in the neuropathology of suicide, or merely follow the changes of comorbid psychiatric disorders. The purpose of this chapter is to review the scattered literature on the involvement of neuroglia in suicide and to describe how these cells might be responsive to the current pharmacologic interventions. We describe the different biological features of neuroglia in relation to suicide and the underlying psychiatric disorders, the molecular commonalities of neuroglial alterations in suicide across different psychiatric disorders, and the evidence for morphologic neuroglia changes in relation to the severity and resilience of suicide. Illuminating the mechanisms by which neuroglia are involved in suicide may ultimately lead to the development of suicide-related biomarkers and novel therapies for suicide prevention.
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Affiliation(s)
- Lin Zhang
- Neuropsychiatric Disorders Lab, Neuroimmunology Group, Netherlands Institute for Neuroscience, An Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Dick F Swaab
- Neuropsychiatric Disorders Lab, Neuroimmunology Group, Netherlands Institute for Neuroscience, An Institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands.
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Sun W, Baranova A, Liu D, Cao H, Zhang X, Zhang F. Phenome-wide investigation of bidirectional causal relationships between major depressive disorder and common human diseases. Transl Psychiatry 2024; 14:506. [PMID: 39730323 PMCID: PMC11680865 DOI: 10.1038/s41398-024-03216-z] [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: 04/18/2024] [Revised: 12/03/2024] [Accepted: 12/17/2024] [Indexed: 12/29/2024] Open
Abstract
The high comorbidity of major depressive disorder (MDD) with other diseases has been well-documented. However, the pairwise causal connections for MDD comorbid networks are poorly characterized. We performed Phenome-wide Mendelian randomization (MR) analyses to explore bidirectional causal associations between MDD (N = 807,553) and 877 common diseases from FinnGen datasets (N = 377,277). The inverse variance weighting method was the primary technique, and other methods (weighted median and MR-Egger) were used for sensitivity analyses. Our MR analyses showed that the genetic liability to MDD is causally associated with the risks of 324 disease phenotypes (average b: 0.339), including 46 psychiatric and behavioral disorders (average b: 0.618), 18 neurological diseases (average b: 0.348), 44 respiratory diseases (average b: 0.345), 40 digestive diseases (average b: 0.281), 18 circulatory diseases (average b: 0.237), 37 genitourinary diseases (average b: 0.271), 66 musculoskeletal and connective diseases (average b: 0.326), 22 endocrine diseases (average b: 0.302), and others. In a reverse analysis, a total of 51 genetic components predisposing to various diseases were causally associated with MDD risk (average b: 0.086), including 5 infectious diseases (average b: 0.056), 11 neurological diseases (average b: 0.106), 14 oncological diseases (average b: 0.108), and 5 psychiatric and behavioral disorders (average b: 0.114). Bidirectional causal associations were identified between MDD and 15 diseases. For most MR analyses, little evidence of heterogeneity and pleiotropy was detected. Our findings confirmed the extensive and significant causal role of genetic predisposition to MDD in contributing to human disease phenotypes, which were more pronounced than those seen in the reverse analysis of the causal influences of other diseases on MDD.
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Affiliation(s)
- Wenxi Sun
- Suzhou Guangji Hospital, Suzhou, Jiangsu Province; Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu Province, China
| | - Ancha Baranova
- School of Systems Biology, George Mason University, Manassas, VA, USA
- Research Centre for Medical Genetics, Moscow, Russia
| | - Dongming Liu
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Department of Neurosurgery, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Hongbao Cao
- School of Systems Biology, George Mason University, Manassas, VA, USA
| | - Xiaobin Zhang
- Suzhou Guangji Hospital, Suzhou, Jiangsu Province; Affiliated Guangji Hospital of Soochow University, Suzhou, Jiangsu Province, China.
| | - Fuquan Zhang
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.
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Tkachev A, Stekolshchikova E, Golubova A, Serkina A, Morozova A, Zorkina Y, Riabinina D, Golubeva E, Ochneva A, Savenkova V, Petrova D, Andreyuk D, Goncharova A, Alekseenko I, Kostyuk G, Khaitovich P. Screening for depression in the general population through lipid biomarkers. EBioMedicine 2024; 110:105455. [PMID: 39571307 PMCID: PMC11617895 DOI: 10.1016/j.ebiom.2024.105455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 10/29/2024] [Accepted: 10/31/2024] [Indexed: 12/08/2024] Open
Abstract
BACKGROUND Anxiety and depression significantly contribute to the overall burden of mental disorders, with depression being one of the leading causes of disability. Despite this, no biochemical test has been implemented for the diagnosis of these mental disorders, while recent studies have highlighted lipids as potential biomarkers. METHODS Using a streamlined high-throughput lipidome analysis method, direct-infusion mass spectrometry, we evaluated blood plasma lipid levels in 604 individuals from a general urban population and analysed their association with self-reported anxiety and depression symptoms. We also assessed lipidome profiles in 32 patients with clinical depression, matched to 21 healthy controls. FINDINGS We found a significant correlation between lipid abundances and the severity of self-reported depression symptoms. Moreover, lipid alterations detected in high scoring volunteers mirrored the lipidome profiles identified in patients with clinical depression included in our study. Based on these findings, we developed a lipid-based predictive model distinguishing individuals reporting severe depressive symptoms from non-depressed subjects with high accuracy. INTERPRETATION This study demonstrates the possibility of generalizing lipid alterations from a clinical cohort to the general population and underscores the potential of lipid-based biomarkers in assessing depressive states. FUNDING This study was sponsored by the Moscow Center for Innovative Technologies in Healthcare, №2707-2, №2102-11.
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Affiliation(s)
- Anna Tkachev
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia; LLC NeurOmix, Moscow, 119571, Russia
| | - Elena Stekolshchikova
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia
| | - Anastasia Golubova
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia
| | - Anna Serkina
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia
| | - Anna Morozova
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia; Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034, Moscow, Russia
| | - Yana Zorkina
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia; Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034, Moscow, Russia
| | - Daria Riabinina
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia
| | - Elizaveta Golubeva
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia
| | - Aleksandra Ochneva
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia; Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, 119034, Moscow, Russia
| | - Valeria Savenkova
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia
| | - Daria Petrova
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia
| | - Denis Andreyuk
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia; Economy Faculty, M.V. Lomonosov Moscow State University, 119991, Moscow, Russia
| | - Anna Goncharova
- Moscow Center for Healthcare Innovations, Moscow, 123473, Russia
| | - Irina Alekseenko
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Science, Moscow Region, 142290, Russia
| | - Georgiy Kostyuk
- Mental-health Clinic No. 1, Named After N.A. Alekseev, Moscow, 117152, Russia.
| | - Philipp Khaitovich
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, 121205, Russia; LLC NeurOmix, Moscow, 119571, Russia.
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5
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Guo Z, Zhang Z, Huang W, Xia H, Huang S, Lan X, Ning Y, Zhou Y, Shang D. Interpretation of the pathogenesis and therapeutic mechanisms of first-episode major depressive disorder based on multiple amino acid metabolic pathways: a metabolomics study. Metab Brain Dis 2024; 40:37. [PMID: 39576355 DOI: 10.1007/s11011-024-01427-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2024] [Accepted: 11/01/2024] [Indexed: 11/24/2024]
Abstract
OBJECTIVES Given the unclear etiology and treatment mechanisms of depression, we aim to explore the metabolic differences between patients with major depressive disorder (MDD) and the healthy population, as well as before and after treatment with escitalopram (ESC). METHODS Recruit first-episode drug-naïve MDD (DN-MDD) patients and healthy controls (HCs). Clinical data and serum samples from all subjects were collected at baseline and patients' samples were collected again after ESC monotherapy for four weeks. Perform non-targeted metabolomic analysis and apply MetaboAnalyst 5.0 to identify differential metabolites and execute KEGG pathway enrichment. RESULTS Through metabolomic analysis of serum samples, 904 differential metabolites were identified in the DN-MDD group compared to the HCs, and 455 metabolites in treated patients compared to DN-MDD patients. In the pathway analysis, DN-MDD state regulated functions of histidine, beta-alanine, aspartate, and tryptophan metabolism, while ESC treatment produced an influence on the biological process of aspartate and sphingolipid. CONCLUSION We respectively depicted metabolism-related biomolecular changes in the serum of patients suffering from MDD and undergoing ESC treatment. Multiple amino acid metabolism pathways were adjusted in MDD patients, and levels of aspartate, arginine and sphingolipids were regulated after ESC monotherapy. These biomolecular changes may bring new insights into the biology and treatment of MDD from the perspective of the serum metabolites.
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Affiliation(s)
- Zhihao Guo
- The Affiliated Brain Hospital, Guangzhou Medical University, 36 Mingxin Road, Guangzhou, 510370, China
- Guangzhou Medical University, 1 Xinzao Road, Guangzhou, China
| | - Zi Zhang
- The Affiliated Brain Hospital, Guangzhou Medical University, 36 Mingxin Road, Guangzhou, 510370, China
- Guangzhou Medical University, 1 Xinzao Road, Guangzhou, China
| | - Wanting Huang
- The Affiliated Brain Hospital, Guangzhou Medical University, 36 Mingxin Road, Guangzhou, 510370, China
- Guangzhou Medical University, 1 Xinzao Road, Guangzhou, China
| | - Hui Xia
- The Affiliated Brain Hospital, Guangzhou Medical University, 36 Mingxin Road, Guangzhou, 510370, China
- Guangzhou Medical University, 1 Xinzao Road, Guangzhou, China
| | - Shanqing Huang
- The Affiliated Brain Hospital, Guangzhou Medical University, 36 Mingxin Road, Guangzhou, 510370, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China
| | - Xiaofeng Lan
- The Affiliated Brain Hospital, Guangzhou Medical University, 36 Mingxin Road, Guangzhou, 510370, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China
| | - Yuping Ning
- The Affiliated Brain Hospital, Guangzhou Medical University, 36 Mingxin Road, Guangzhou, 510370, China.
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China.
| | - Yanling Zhou
- The Affiliated Brain Hospital, Guangzhou Medical University, 36 Mingxin Road, Guangzhou, 510370, China.
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China.
| | - Dewei Shang
- The Affiliated Brain Hospital, Guangzhou Medical University, 36 Mingxin Road, Guangzhou, 510370, China.
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China.
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Herman RJ, Schmidt HD. Targeting GLP-1 receptors to reduce nicotine use disorder: Preclinical and clinical evidence. Physiol Behav 2024; 281:114565. [PMID: 38663460 PMCID: PMC11128349 DOI: 10.1016/j.physbeh.2024.114565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/18/2024] [Accepted: 04/22/2024] [Indexed: 04/30/2024]
Abstract
Nicotine use disorder (NUD) remains a leading cause of preventable death in the U.S. Unfortunately, current FDA-approved pharmacotherapies for smoking cessation have limited efficacy and are associated with high rates of relapse. One major barrier to long-term smoking abstinence is body weight gain during withdrawal. Nicotine withdrawal-induced body weight gain can also lead to development of chronic disease states like obesity and type II diabetes mellitus. Therefore, it is critical to identify novel pharmacotherapies for NUD that decrease relapse and nicotine withdrawal symptoms including body weight gain. Recent studies demonstrate that glucagon-like peptide-1 receptor (GLP-1R) agonists attenuate voluntary nicotine taking and seeking and prevent withdrawal-induced hyperphagia and body weight gain. Emerging evidence also suggests that GLP-1R agonists improve cognitive deficits, as well as depressive- and anxiety-like behaviors, which contribute to smoking relapse during withdrawal. While further studies are necessary to fully characterize the effects of GLP-1R agonists on NUD and understand the mechanisms by which GLP-1R agonists decrease nicotine withdrawal-mediated behaviors, the current literature supports GLP-1R-based approaches to treating NUD.
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Affiliation(s)
- Rae J Herman
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Heath D Schmidt
- Department of Biobehavioral Health Sciences, School of Nursing, University of Pennsylvania, Philadelphia, PA, United States; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
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7
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Ehtemam H, Sadeghi Esfahlani S, Sanaei A, Ghaemi MM, Hajesmaeel-Gohari S, Rahimisadegh R, Bahaadinbeigy K, Ghasemian F, Shirvani H. Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies. BMC Med Inform Decis Mak 2024; 24:138. [PMID: 38802823 PMCID: PMC11129374 DOI: 10.1186/s12911-024-02524-0] [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: 09/26/2023] [Accepted: 04/30/2024] [Indexed: 05/29/2024] Open
Abstract
OBJECTIVE Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction of suicide attempts. METHOD A systematic review was performed using PubMed, Scopus, Web of Science and SID databases. We aim to evaluate the performance of ML algorithms and summarize their effects, gather relevant and reliable information to synthesize existing evidence, identify knowledge gaps, and provide a comprehensive list of the suicide risk factors using mixed method approach. RESULTS Forty-one studies published between 2011 and 2022, which matched inclusion criteria, were chosen as suitable. We included studies aimed at predicting the suicide risk by machine learning algorithms except natural language processing (NLP) and image processing. The neural network (NN) algorithm exhibited the lowest accuracy at 0.70, whereas the random forest demonstrated the highest accuracy, reaching 0.94. The study assessed the COX and random forest models and observed a minimum area under the curve (AUC) value of 0.54. In contrast, the XGBoost classifier yielded the highest AUC value, reaching 0.97. These specific AUC values emphasize the algorithm-specific performance in capturing the trade-off between sensitivity and specificity for suicide risk prediction. Furthermore, our investigation identified several common suicide risk factors, including age, gender, substance abuse, depression, anxiety, alcohol consumption, marital status, income, education, and occupation. This comprehensive analysis contributes valuable insights into the multifaceted nature of suicide risk, providing a foundation for targeted preventive strategies and intervention efforts. CONCLUSIONS The effectiveness of ML algorithms and their application in predicting suicide risk has been controversial. There is a need for more studies on these algorithms in clinical settings, and the related ethical concerns require further clarification.
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Affiliation(s)
- Houriyeh Ehtemam
- School of Engineering and the Built Environment, Anglia Ruskin University, Chelmsford, UK
| | | | - Alireza Sanaei
- School of Engineering and the Built Environment, Anglia Ruskin University, Chelmsford, UK
| | - Mohammad Mehdi Ghaemi
- Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
| | - Sadrieh Hajesmaeel-Gohari
- Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Rohaneh Rahimisadegh
- Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Kambiz Bahaadinbeigy
- Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Fahimeh Ghasemian
- Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Hassan Shirvani
- School of Engineering and the Built Environment, Anglia Ruskin University, Chelmsford, UK
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8
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Pigoni A, Delvecchio G, Turtulici N, Madonna D, Pietrini P, Cecchetti L, Brambilla P. Machine learning and the prediction of suicide in psychiatric populations: a systematic review. Transl Psychiatry 2024; 14:140. [PMID: 38461283 PMCID: PMC10925059 DOI: 10.1038/s41398-024-02852-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 02/22/2024] [Accepted: 02/22/2024] [Indexed: 03/11/2024] Open
Abstract
Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric populations, a formal psychiatric diagnosis emerged as a strong predictor of suicidal risk, overshadowing more subtle risk factors specific to distinct populations. To overcome this limitation, we conducted a systematic review of ML studies evaluating suicidal behaviors exclusively in psychiatric clinical populations. A systematic literature search was performed from inception through November 17, 2022 on PubMed, EMBASE, and Scopus following the PRISMA guidelines. Original research using ML techniques to assess the risk of suicide or predict suicide attempts in the psychiatric population were included. An assessment for bias risk was performed using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. About 1032 studies were retrieved, and 81 satisfied the inclusion criteria and were included for qualitative synthesis. Clinical and demographic features were the most frequently employed and random forest, support vector machine, and convolutional neural network performed better in terms of accuracy than other algorithms when directly compared. Despite heterogeneity in procedures, most studies reported an accuracy of 70% or greater based on features such as previous attempts, severity of the disorder, and pharmacological treatments. Although the evidence reported is promising, ML algorithms for suicidal prediction still present limitations, including the lack of neurobiological and imaging data and the lack of external validation samples. Overcoming these issues may lead to the development of models to adopt in clinical practice. Further research is warranted to boost a field that holds the potential to critically impact suicide mortality.
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Affiliation(s)
- Alessandro Pigoni
- Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Nunzio Turtulici
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Domenico Madonna
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Pietro Pietrini
- MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Luca Cecchetti
- Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
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9
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Ho CSH, Tan TWK, Khoe HCH, Chan YL, Tay GWN, Tang TB. Using an Interpretable Amino Acid-Based Machine Learning Method to Enhance the Diagnosis of Major Depressive Disorder. J Clin Med 2024; 13:1222. [PMID: 38592058 PMCID: PMC10931723 DOI: 10.3390/jcm13051222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/13/2024] [Accepted: 02/19/2024] [Indexed: 04/10/2024] Open
Abstract
Background: Major depressive disorder (MDD) is a leading cause of disability worldwide. At present, however, there are no established biomarkers that have been validated for diagnosing and treating MDD. This study sought to assess the diagnostic and predictive potential of the differences in serum amino acid concentration levels between MDD patients and healthy controls (HCs), integrating them into interpretable machine learning models. Methods: In total, 70 MDD patients and 70 HCs matched in age, gender, and ethnicity were recruited for the study. Serum amino acid profiling was conducted by means of chromatography-mass spectrometry. A total of 21 metabolites were analysed, with 17 from a preset amino acid panel and the remaining 4 from a preset kynurenine panel. Logistic regression was applied to differentiate MDD patients from HCs. Results: The best-performing model utilised both feature selection and hyperparameter optimisation and yielded a moderate area under the receiver operating curve (AUC) classification value of 0.76 on the testing data. The top five metabolites identified as potential biomarkers for MDD were 3-hydroxy-kynurenine, valine, kynurenine, glutamic acid, and xanthurenic acid. Conclusions: Our study highlights the potential of using an interpretable machine learning analysis model based on amino acids to aid and increase the diagnostic accuracy of MDD in clinical practice.
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Affiliation(s)
- Cyrus Su Hui Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117543, Singapore;
| | - Trevor Wei Kiat Tan
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117543, Singapore;
- Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117543, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore 117456, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore
| | - Howard Cai Hao Khoe
- Singapore Psychiatry Residency, National Healthcare Group, Singapore 308433, Singapore;
| | - Yee Ling Chan
- Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS (UTP), Seri Iskandar 32610, Perak, Malaysia; (Y.L.C.); (T.B.T.)
| | - Gabrielle Wann Nii Tay
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117543, Singapore;
| | - Tong Boon Tang
- Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS (UTP), Seri Iskandar 32610, Perak, Malaysia; (Y.L.C.); (T.B.T.)
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10
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Kang S, Kim W, Nam J, Li K, Kang Y, Bae B, Chun KH, Chung C, Lee J. Non-Targeted Metabolomics Investigation of a Sub-Chronic Variable Stress Model Unveils Sex-Dependent Metabolic Differences Induced by Stress. Int J Mol Sci 2024; 25:2443. [PMID: 38397124 PMCID: PMC10889542 DOI: 10.3390/ijms25042443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/16/2024] [Accepted: 02/18/2024] [Indexed: 02/25/2024] Open
Abstract
Depression is twice as prevalent in women as in men, however, most preclinical studies of depression have used male rodent models. This study aimed to examine how stress affects metabolic profiles depending on sex using a rodent depression model: sub-chronic variable stress (SCVS). The SCVS model of male and female mice was established in discovery and validation sets. The stress-induced behavioral phenotypic changes were similar in both sexes, however, the metabolic profiles of female plasma and brain became substantially different after stress, whereas those of males did not. Four stress-differential plasma metabolites-β-hydroxybutyric acid (BHB), L-serine, glycerol, and myo-inositol-could yield biomarker panels with excellent performance to discern the stressed individuals only for females. Disturbances in BHB, glucose, 1,5-anhydrosorbitol, lactic acid, and several fatty acids in the plasma of stressed females implied a systemic metabolic shift to β-oxidation in females. The plasma levels of BHB and corticosterone only in stressed females were observed not only in SCVS but also in an acute stress model. These results collectively suggest a sex difference in the metabolic responses by stress, possibly involving the energy metabolism shift to β-oxidation and the HPA axis dysregulation in females.
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Affiliation(s)
- Seulgi Kang
- School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea; (S.K.); (K.L.); (Y.K.); (B.B.)
| | - Woonhee Kim
- Department of Biological Sciences, Konkuk University, Seoul 05029, Republic of Korea; (W.K.); (J.N.); (C.H.C.)
| | - Jimin Nam
- Department of Biological Sciences, Konkuk University, Seoul 05029, Republic of Korea; (W.K.); (J.N.); (C.H.C.)
| | - Ke Li
- School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea; (S.K.); (K.L.); (Y.K.); (B.B.)
| | - Yua Kang
- School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea; (S.K.); (K.L.); (Y.K.); (B.B.)
| | - Boyeon Bae
- School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea; (S.K.); (K.L.); (Y.K.); (B.B.)
| | - Kwang-Hoon Chun
- Gachon Institute of Pharmaceutical Sciences, College of Pharmacy, Gachon University, Incheon 21936, Republic of Korea;
| | - ChiHye Chung
- Department of Biological Sciences, Konkuk University, Seoul 05029, Republic of Korea; (W.K.); (J.N.); (C.H.C.)
| | - Jeongmi Lee
- School of Pharmacy, Sungkyunkwan University, Suwon 16419, Republic of Korea; (S.K.); (K.L.); (Y.K.); (B.B.)
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11
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Delanote J, Correa Rojo A, Wells PM, Steves CJ, Ertaylan G. Systematic identification of the role of gut microbiota in mental disorders: a TwinsUK cohort study. Sci Rep 2024; 14:3626. [PMID: 38351227 PMCID: PMC10864280 DOI: 10.1038/s41598-024-53929-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/06/2024] [Indexed: 02/16/2024] Open
Abstract
Mental disorders are complex disorders influenced by multiple genetic, environmental, and biological factors. Specific microbiota imbalances seem to affect mental health status. However, the mechanisms by which microbiota disturbances impact the presence of depression, stress, anxiety, and eating disorders remain poorly understood. Currently, there are no robust biomarkers identified. We proposed a novel pyramid-layer design to accurately identify microbial/metabolomic signatures underlying mental disorders in the TwinsUK registry. Monozygotic and dizygotic twins discordant for mental disorders were screened, in a pairwise manner, for differentially abundant bacterial genera and circulating metabolites. In addition, multivariate analyses were performed, accounting for individual-level confounders. Our pyramid-layer study design allowed us to overcome the limitations of cross-sectional study designs with significant confounder effects and resulted in an association of the abundance of genus Parabacteroides with the diagnosis of mental disorders. Future research should explore the potential role of Parabacteroides as a mediator of mental health status. Our results indicate the potential role of the microbiome as a modifier in mental disorders that might contribute to the development of novel methodologies to assess personal risk and intervention strategies.
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Affiliation(s)
- Julie Delanote
- Sustainable Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Alejandro Correa Rojo
- Sustainable Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
- Data Science Institute, Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Diepenbeek, Belgium
| | - Philippa M Wells
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital, 3-4th Floor South Wing Block D, Westminster Bridge Road, London, SE1 7EH, UK
| | - Claire J Steves
- Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital, 3-4th Floor South Wing Block D, Westminster Bridge Road, London, SE1 7EH, UK
- Department of Ageing and Health, St Thomas' Hospital, 9th floor, North Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Gökhan Ertaylan
- Sustainable Health, Flemish Institute for Technological Research (VITO), Mol, Belgium.
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12
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Chernonosov AA, Mednova IA, Levchuk LA, Mazurenko EO, Roschina OV, Simutkin GG, Bokhan NA, Koval VV, Ivanova SA. Untargeted Plasma Metabolomic Profiling in Patients with Depressive Disorders: A Preliminary Study. Metabolites 2024; 14:110. [PMID: 38393002 PMCID: PMC10890195 DOI: 10.3390/metabo14020110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 02/02/2024] [Accepted: 02/04/2024] [Indexed: 02/25/2024] Open
Abstract
Depressive disorder is a multifactorial disease that is based on dysfunctions in mental and biological processes. The search for biomarkers can improve its diagnosis, personalize therapy, and lead to a deep understanding of the biochemical processes underlying depression. The purpose of this work was a metabolomic analysis of blood serum to classify patients with depressive disorders and healthy individuals using Compound Discoverer software. Using high-resolution mass spectrometry, blood plasma samples from 60 people were analyzed, of which 30 were included in a comparison group (healthy donors), and 30 were patients with a depressive episode (F32.11) and recurrent depressive disorder (F33.11). Differences between patient and control groups were identified using the built-in utilities in Compound Discoverer software. Compounds were identified by their accurate mass and fragment patterns using the mzCloud database and tentatively identified by their exact mass using the ChemSpider search engine and the KEGG, ChEBI, FDA UNII-NLM, Human Metabolome and LipidMAPS databases. We identified 18 metabolites that could divide patients with depressive disorders from healthy donors. Of these, only two compounds were tentatively identified using the mzCloud database (betaine and piperine) based on their fragmentation spectra. For three compounds ((4S,5S,8S,10R)-4,5,8-trihydroxy-10-methyl-3,4,5,8,9,10-hexahydro-2H-oxecin-2-one, (2E,4E)-N-(2-hydroxy-2-methylpropyl)-2,4-tetradecadienamide and 17α-methyl-androstan-3-hydroxyimine-17β-ol), matches were found in the mzCloud database but with low score, which could not serve as reliable evidence of their structure. Another 13 compounds were identified by their exact mass in the ChemSpider database, 9 (g-butyrobetaine, 6-diazonio-5-oxo-L-norleucine, 11-aminoundecanoic acid, methyl N-acetyl-2-diazonionorleucinate, glycyl-glycyl-argininal, dilaurylmethylamine, 12-ketodeoxycholic acid, dicetylamine, 1-linoleoyl-2-hydroxy-sn-glycero-3-PC) had only molecular formulas proposed, and 4 were unidentified. Thus, the use of Compound Discoverer software alone was not sufficient to identify all revealed metabolites. Nevertheless, the combination of the found metabolites made it possible to divide patients with depressive disorders from healthy donors.
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Affiliation(s)
- Alexander A Chernonosov
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of Russian Academy of Sciences, Lavrentyev Avenue 8, Novosibirsk 630090, Russia
| | - Irina A Mednova
- Mental Health Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Aleutskaya Str. 4, Tomsk 634014, Russia
| | - Lyudmila A Levchuk
- Mental Health Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Aleutskaya Str. 4, Tomsk 634014, Russia
| | - Ekaterina O Mazurenko
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of Russian Academy of Sciences, Lavrentyev Avenue 8, Novosibirsk 630090, Russia
| | - Olga V Roschina
- Mental Health Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Aleutskaya Str. 4, Tomsk 634014, Russia
| | - German G Simutkin
- Mental Health Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Aleutskaya Str. 4, Tomsk 634014, Russia
| | - Nikolay A Bokhan
- Mental Health Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Aleutskaya Str. 4, Tomsk 634014, Russia
- Department of Psychiatry, Addictology and Psychotherapy, Siberian State Medical University, Moskovsky Trakt 2, Tomsk 634050, Russia
| | - Vladimir V Koval
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of Russian Academy of Sciences, Lavrentyev Avenue 8, Novosibirsk 630090, Russia
| | - Svetlana A Ivanova
- Mental Health Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Aleutskaya Str. 4, Tomsk 634014, Russia
- Department of Psychiatry, Addictology and Psychotherapy, Siberian State Medical University, Moskovsky Trakt 2, Tomsk 634050, Russia
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13
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Pereira CA, Reis-de-Oliveira G, Pierone BC, Martins-de-Souza D, Kaster MP. Depicting the molecular features of suicidal behavior: a review from an "omics" perspective. Psychiatry Res 2024; 332:115682. [PMID: 38198856 DOI: 10.1016/j.psychres.2023.115682] [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: 07/09/2023] [Revised: 12/05/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024]
Abstract
Background Suicide is one of the leading global causes of death. Behavior patterns from suicide ideation to completion are complex, involving multiple risk factors. Advances in technologies and large-scale bioinformatic tools are changing how we approach biomedical problems. The "omics" field may provide new knowledge about suicidal behavior to improve identification of relevant biological pathways associated with suicidal behavior. Methods We reviewed transcriptomic, proteomic, and metabolomic studies conducted in blood and post-mortem brains from individuals who experienced suicide or suicidal behavior. Omics data were combined using systems biology in silico, aiming at identifying major biological mechanisms and key molecules associated with suicide. Results Post-mortem samples of suicide completers indicate major dysregulations in pathways associated with glial cells (astrocytes and microglia), neurotransmission (GABAergic and glutamatergic systems), neuroplasticity and cell survivor, immune responses and energy homeostasis. In the periphery, studies found alterations in molecules involved in immune responses, polyamines, lipid transport, energy homeostasis, and amino and nucleic acid metabolism. Limitations We included only exploratory, non-hypothesis-driven studies; most studies only included one brain region and whole tissue analysis, and focused on suicide completers who were white males with almost none confounding factors. Conclusions We can highlight the importance of synaptic function, especially the balance between the inhibitory and excitatory synapses, and mechanisms associated with neuroplasticity, common pathways associated with psychiatric disorders. However, some of the pathways highlighted in this review, such as transcriptional factors associated with RNA splicing, formation of cortical connections, and gliogenesis, point to mechanisms that still need to be explored.
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Affiliation(s)
- Caibe Alves Pereira
- Laboratory of Translational Neurosciences, Department of Biochemistry, Federal University of Santa Catarina (UFSC), Florianopolis, Santa Catarina, Brazil
| | - Guilherme Reis-de-Oliveira
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Bruna Caroline Pierone
- Laboratory of Translational Neurosciences, Department of Biochemistry, Federal University of Santa Catarina (UFSC), Florianopolis, Santa Catarina, Brazil
| | - Daniel Martins-de-Souza
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil; Instituto Nacional de Biomarcadores Em Neuropsiquiatria (INBION) Conselho Nacional de Desenvolvimento Científico E Tecnológico, São Paulo, Brazil; Experimental Medicine Research Cluster (EMRC), University of Campinas, Campinas, SP, Brazil; D'Or Institute for Research and Education (IDOR), São Paulo, Brazil; INCT in Modelling Human Complex Diseases with 3D Platforms (Model3D), São Paulo, Brazil.
| | - Manuella Pinto Kaster
- Laboratory of Translational Neurosciences, Department of Biochemistry, Federal University of Santa Catarina (UFSC), Florianopolis, Santa Catarina, Brazil.
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14
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You Z, Wang C, Lan X, Li W, Shang D, Zhang F, Ye Y, Liu H, Zhou Y, Ning Y. The contribution of polyamine pathway to determinations of diagnosis for treatment-resistant depression: A metabolomic analysis. Prog Neuropsychopharmacol Biol Psychiatry 2024; 128:110849. [PMID: 37659714 DOI: 10.1016/j.pnpbp.2023.110849] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 08/26/2023] [Accepted: 08/28/2023] [Indexed: 09/04/2023]
Abstract
OBJECTIVES Approximately one-third of major depressive disorder (MDD) patients do not respond to standard antidepressants and develop treatment-resistant depression (TRD). We aimed to reveal metabolic differences and discover promising potential biomarkers in TRD. METHODS Our study recruited 108 participants including healthy controls (n = 40) and patients with TRD (n = 35) and first-episode drug-naive MDD (DN-MDD) (n = 33). Plasma samples were presented to ultra performance liquid chromatography-tandem mass spectrometry. Then, a machine-learning algorithm was conducted to facilitate the selection of potential biomarkers. RESULTS TRD appeared to be a distinct metabolic disorder from DN-MDD and healthy controls (HCs). Compared to HCs, 199 and 176 differentially expressed metabolites were identified in TRD and DN-MDD, respectively. Of all the metabolites that were identified, spermine, spermidine, and carnosine were considered the most promising biomarkers for diagnosing TRD and DN-MDD patients, with the resulting area under the ROC curve of 0.99, 0.99, and 0.93, respectively. Metabolic pathway analysis yielded compelling evidence of marked changes or imbalances in both polyamine metabolism and energy metabolism, which could potentially represent the primary altered pathways associated with MDD. Additionally, L-glutamine, Beta-alanine, and spermine were correlated with HAMD score. CONCLUSIONS A more disordered metabolism structure is found in TRD than in DN-MDD and HCs. Future investigations should prioritize the comprehensive analysis of potential roles played by these differential metabolites and disturbances in polyamine pathways in the pathophysiology of TRD and depression.
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Affiliation(s)
- Zerui You
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Chengyu Wang
- Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Xiaofeng Lan
- Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Weicheng Li
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Dewei Shang
- Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Fan Zhang
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Yanxiang Ye
- Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Haiyan Liu
- Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Yanling Zhou
- Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.
| | - Yuping Ning
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, China; Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China; Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.
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15
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Jitte S, Keluth S, Bisht P, Wal P, Singh S, Murti K, Kumar N. Obesity and Depression: Common Link and Possible Targets. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2024; 23:1425-1449. [PMID: 38747226 DOI: 10.2174/0118715273291985240430074053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 03/15/2024] [Accepted: 03/27/2024] [Indexed: 10/22/2024]
Abstract
Depression is among the main causes of disability, and its protracted manifestations could make it even harder to treat metabolic diseases. Obesity is linked to episodes of depression, which is closely correlated to abdominal adiposity and impaired food quality. The present review is aimed at studying possible links between obesity and depression along with targets to disrupt it. Research output in Pubmed and Scopus were referred for writing this manuscript. Obesity and depression are related, with the greater propensity of depressed people to gain weight, resulting in poor dietary decisions and a sedentary lifestyle. Adipokines, which include adiponectin, resistin, and leptin are secretory products of the adipose tissue. These adipokines are now being studied to learn more about the connection underlying obesity and depression. Ghrelin, a gut hormone, controls both obesity and depression. Additionally, elevated ghrelin levels result in anxiolytic and antidepressant-like effects. The gut microbiota influences the metabolic functionalities of a person, like caloric processing from indigestible nutritional compounds and storage in fatty tissue, that exposes an individual to obesity, and gut microorganisms might connect to the CNS through interconnecting pathways, including neurological, endocrine, and immunological signalling systems. The alteration of brain activity caused by gut bacteria has been related to depressive episodes. Monoamines, including dopamine, serotonin, and norepinephrine, have been widely believed to have a function in emotions and appetite control. Emotional signals stimulate arcuate neurons in the hypothalamus that are directly implicated in mood regulation and eating. The peptide hormone GLP-1(glucagon-like peptide- 1) seems to have a beneficial role as a medical regulator of defective neuroinflammation, neurogenesis, synaptic dysfunction, and neurotransmitter secretion discrepancy in the depressive brain. The gut microbiota might have its action in mood and cognition regulation, in addition to its traditional involvement in GI function regulation. This review addressed the concept that obesity-related low-grade mild inflammation in the brain contributes to chronic depression and cognitive impairments.
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Affiliation(s)
- Srikanth Jitte
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research, Hajipur, Vaishali 844102, Bihar, India
| | - Saritha Keluth
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research, Hajipur, Vaishali 844102, Bihar, India
| | - Priya Bisht
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research, Hajipur, Vaishali 844102, Bihar, India
| | - Pranay Wal
- PSIT- Pranveer Singh Institute of Technology, Pharmacy, Kanpur 209305, Uttar Pradesh, India
| | - Sanjiv Singh
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research, Hajipur, Vaishali 844102, Bihar, India
| | - Krishna Murti
- Department of Pharmacy Practice, National Institute of Pharmaceutical Education and Research, Hajipur, Vaishali 844102, Bihar, India
| | - Nitesh Kumar
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research, Hajipur, Vaishali 844102, Bihar, India
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16
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Liu X, Zhang B, Tian J, Han Y. Plasma metabolomics reveals the intervention mechanism of different types of exercise on chronic unpredictable mild stress-induced depression rat model. Metab Brain Dis 2024; 39:1-13. [PMID: 37999885 DOI: 10.1007/s11011-023-01310-7] [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] [Accepted: 10/08/2023] [Indexed: 11/25/2023]
Abstract
OBJECTIVE To study the effects of different types of exercise on the plasma metabolomics of chronic unpredictable mild stress (CUMS)-induced depressed rats based on 1H-NMR metabolomics techniques, and to explore the potential mechanisms of exercise for the treatment of depression. Rats were randomly divided into blank control group (C), CUMS control group (D), pre-exercise with CUMS group (P), CUMS with aerobic exercise group, CUMS with resistance exercise group (R), and CUMS with aerobic + resistance exercise group (E). The corresponding protocol intervention was applied to each group of rats. Body weight, sucrose preference and open field tests were performed weekly during the experiment to evaluate the extent of depression in rats. Plasma samples from each group of rats were collected at the end of the experiment, and then the plasma was analyzed by 1H-NMR metabolomics combined with multivariate statistical analysis methods to identify differential metabolites and perform metabolic pathway analysis. (1) Compared with the group D, the body weight, sucrose preference rate, and the number of crossings and standings in the different types of exercise groups were significantly improved (p < 0.05 or p < 0.01). (2) Compared to group C, a total of 15 differential metabolites associated with depression were screened in the plasma of rats in group D, involving 6 metabolic pathways. Group P can regulate the levels of 6 metabolites: valine, lactate, inositol, glucose, phosphocreatine, acetoacetic acid. Group A can regulate the levels of 6 metabolites: N-acetylglycoprotein, leucine, lactate, low density lipoprotein, glucose and acetoacetic acid. Group R can regulate the levels of 6 metabolites: choline, lactate, inositol, glucose, phosphocreatine and acetoacetic acid. Group E can regulate the levels of 5 metabolites: choline, citric acid, glucose, acetone and acetoacetic acid. The different types of exercise groups can improve the depressive symptoms in CUMS rats, and there are common metabolites and metabolic pathways for their mechanism of effects. This study provides a powerful analytical tool to study the mechanism of the antidepressant effect of exercise, and provides an important method and basis for the early diagnosis, prevention and treatment of depression.
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Affiliation(s)
- Xiangyu Liu
- School of Physical Education, Huainan Normal University, Huainan, China.
| | - Bo Zhang
- Changji Vocational and Technical College, Xinjiang, China
| | - Junsheng Tian
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan, China
| | - Yumei Han
- School of Physical Education, Shanxi University, Taiyuan, China
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17
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Shirayama Y, Iwata M, Miyano K, Hirose Y, Oda Y, Fujita Y, Hashimoto K. Infusions of beta-hydroxybutyrate, an endogenous NLRP3 inflammasome inhibitor, produce antidepressant-like effects on learned helplessness rats through BDNF-TrkB signaling and AMPA receptor activation, and strengthen learning ability. Brain Res 2023; 1821:148567. [PMID: 37689333 DOI: 10.1016/j.brainres.2023.148567] [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: 05/09/2023] [Revised: 08/27/2023] [Accepted: 09/06/2023] [Indexed: 09/11/2023]
Abstract
Beta-hydroxybutyrate (BHB), an endogenous NLRP3 inflammasome inhibitor, has been shown to be associated with the pathophysiology of depression in rodents. However its active mechanism has not been revealed. Herein, we probed both the pathways and brain regions involved in BHB's antidepressant-like effects in a learned helplessness (LH) rat model of depression. A single bilateral infusion of BHB into the cerebral ventricles induced the antidepressant-like effects on the LH rats. The antidepressant-like effects of BHB were blocked by the TrkB inhibitor ANA-12 and the AMPA receptor antagonist NBQX, indicating that the antidepressant-like effects of BHB involve BDNF-TrkB signaling and AMPA receptor activation. Further, infusions of BHB into the prelimbic and infralimbic portions of medial prefrontal cortex, the dentate gyrus of hippocampus, and the basolateral region of amygdala produced the antidepressant-like effects on LH rats. However, infusions of BHB into the central region of amygdala, the CA3 region of hippocampus, and the shell and core regions of nucleus accumbens had no effect. Finally, a single bilateral infusion of BHB into the cerebral ventricles of naive rats strengthened learning ability on repeated active avoidance test where saline-infused animals failed to increase avoidance responses.
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Affiliation(s)
- Yukihiko Shirayama
- Department of Psychiatry, Teikyo University Chiba Medical Center, Ichihara, Japan; Division of Clinical Neuroscience, Chiba University Center for Forensic Mental Health, Chiba, Japan.
| | - Masaaki Iwata
- Department of Neuropsychiatry, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Kanako Miyano
- Department of Pain Control Research, The Jikei University School of Medicine, Tokyo, Japan
| | - Yuki Hirose
- Department of Psychiatry, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Yasunori Oda
- Department of Psychiatry, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Yuko Fujita
- Division of Clinical Neuroscience, Chiba University Center for Forensic Mental Health, Chiba, Japan
| | - Kenji Hashimoto
- Division of Clinical Neuroscience, Chiba University Center for Forensic Mental Health, Chiba, Japan
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18
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Khantakova JN, Mutovina A, Ayriyants KA, Bondar NP. Th17 Cells, Glucocorticoid Resistance, and Depression. Cells 2023; 12:2749. [PMID: 38067176 PMCID: PMC10706111 DOI: 10.3390/cells12232749] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 11/27/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
Depression is a severe mental disorder that disrupts mood and social behavior and is one of the most common neuropsychological symptoms of other somatic diseases. During the study of the disease, a number of theories were put forward (monoamine, inflammatory, vascular theories, etc.), but none of those theories fully explain the pathogenesis of the disease. Steroid resistance is a characteristic feature of depression and can affect not only brain cells but also immune cells. T-helper cells 17 type (Th17) are known for their resistance to the inhibitory effects of glucocorticoids. Unlike the inhibitory effect on other subpopulations of T-helper cells, glucocorticoids can enhance the differentiation of Th17 lymphocytes, their migration to the inflammation, and the production of IL-17A, IL-21, and IL-23 in GC-resistant disease. According to the latest data, in depression, especially the treatment-resistant type, the number of Th17 cells in the blood and the production of IL-17A is increased, which correlates with the severity of the disease. However, there is still a significant gap in knowledge regarding the exact mechanisms by which Th17 cells can influence neuroinflammation in depression. In this review, we discuss the mutual effect of glucocorticoid resistance and Th17 lymphocytes on the pathogenesis of depression.
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Affiliation(s)
- Julia N. Khantakova
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Prospekt Lavrentyeva 10, Novosibirsk 630090, Russia; (K.A.A.); (N.P.B.)
| | - Anastasia Mutovina
- Department of Natural Sciences, Novosibirsk State University, Pirogova Street 2, Novosibirsk 630090, Russia;
| | - Kseniya A. Ayriyants
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Prospekt Lavrentyeva 10, Novosibirsk 630090, Russia; (K.A.A.); (N.P.B.)
| | - Natalia P. Bondar
- Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences (SB RAS), Prospekt Lavrentyeva 10, Novosibirsk 630090, Russia; (K.A.A.); (N.P.B.)
- Department of Natural Sciences, Novosibirsk State University, Pirogova Street 2, Novosibirsk 630090, Russia;
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Tamaki K, Saito N, Tomita H. Serum 3-Hydroxybutyrate is Expected to Serve as One of the Supportive Diagnostic Markers of Persistent Idiopathic Dentoalveolar Pain (PDAP). J Pain Res 2023; 16:4005-4013. [PMID: 38026450 PMCID: PMC10676723 DOI: 10.2147/jpr.s436034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/12/2023] [Indexed: 12/01/2023] Open
Abstract
Background Persistent idiopathic dentoalveolar pain (PDAP), previously referred to as atypical odontalgia, is a chronic dental pain that occurs without signs of pathology. PDAP is considered a diagnosis of exclusion, and its definition is currently under refinement and remains ambiguous. The metabolite known as 3-hydroxybutyrate (3HB) has garnered significant interest as a potential indicator for both depression and chronic psychogenic pain. We investigated the characteristics of patients with PDAP and hypothesized that serum 3HB could support the diagnosis of PDAP. Subjects and Methods Forty-one patients with PDAP and 167 patients with odontogenic toothache were investigated regarding depression and anxiety scales in addition to the general dental evaluation. Blood tests including high-sensitivity CRP, HbA1c, and 3HB were performed for all patients. Associations between PDAP and patients' varying characteristics were investigated using hierarchical multivariate logistic regression analyses. Results There were more females, current smokers, patients with orofacial pain (such as temporomandibular joint pain, glossalgia, and headache), and people with elevated 3HB levels among patients with PDAP than among control participants. Multivariate logistic regression analyses predicting patients with PDAP identified the female sex (odds ratio [OR]: 4.16), current smoking (OR: 14.9), glossalgia (OR: 19.8) a high CES-D score (≥16) (OR: 5.98), and elevated serum 3HB (≥80 μmol/L) (OR: 18.4) factors significantly associated with PDAP. Conclusion Our results demonstrated that serum 3HB levels could be elevated in patients with PDAP compared to other types of odontogenic pain, although 3HB was not specific to PDAP. Based on our findings, five factors - female sex, current smoking, depressive tendencies, chronic orofacial pains, and high serum 3HB levels - could be useful for diagnosing PDAP.
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Affiliation(s)
- Katsuya Tamaki
- Department of Clinical Laboratory Medicine Hirosaki University Graduate School of Medicine, Hirosaki City, Aomori, 036-8562, Japan
- Tamaki Dental Clinic, Keison-Kai, Akita City, 010-0925, Japan
| | - Norihiro Saito
- Department of Clinical Laboratory Medicine Hirosaki University Graduate School of Medicine, Hirosaki City, Aomori, 036-8562, Japan
| | - Hirofumi Tomita
- Department of Clinical Laboratory Medicine Hirosaki University Graduate School of Medicine, Hirosaki City, Aomori, 036-8562, Japan
- Department of Cardiology and Nephrology, Hirosaki University Graduate School of Medicine, Hirosaki City, Aomori, 036-8562, Japan
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20
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Sun XL, Ma LN, Chen ZZ, Xiong YB, Jia J, Wang Y, Ren Y. Search for serum biomarkers in patients with bipolar disorder and major depressive disorder using metabolome analysis. Front Psychiatry 2023; 14:1251955. [PMID: 37736060 PMCID: PMC10509760 DOI: 10.3389/fpsyt.2023.1251955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 08/21/2023] [Indexed: 09/23/2023] Open
Abstract
Objective Bipolar disorder (BD) and major depressive disorder (MDD) are two common psychiatric disorders. Due to the overlapping clinical symptoms and the lack of objective diagnostic biomarkers, bipolar disorder (BD) is easily misdiagnosed as major depressive disorder (MDD), which in turn affects treatment decisions and prognosis. This study aimed to investigate biomarkers that could be used to differentiate BD from MDD. Methods Nuclear magnetic resonance (NMR) spectroscopy was performed to assess serum metabolic profiles in depressed patients with BD (n = 59), patients with MDD (n = 14), and healthy controls (n = 10). Data was analyzed using partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis and t-tests. Different metabolites (VIP > 1 and p < 0.05) were identified and further analyzed using Metabo Analyst 5.0 to identify relevant metabolic pathways. Results The metabolic phenotypes of the BD and MDD groups were significantly different from those of the healthy controls, and there were different metabolite differences between them. In the BD group, the levels of 3-hydroxybutyric acid, n-acetyl glycoprotein, β-glucose, pantothenic acid, mannose, glycerol, and lipids were significantly higher than those in the healthy control group, and the levels of lactate and acetoacetate were significantly lower than those in the healthy control group. In the MDD group, the levels of 3-hydroxybutyric acid, n-acetyl glycoprotein, pyruvate, choline, acetoacetic acid, and lipids were significantly higher than those of healthy controls, and the levels of acetic acid and glycerol were significantly lower than those of healthy controls. Conclusion Glycerolipid metabolism is significantly involved in BD and MDD. Pyruvate metabolism is significantly involved in MDD. Pyruvate, choline, and acetate may be potential biomarkers for MDD to distinguish from BD, and pantothenic acid may be a potential biomarker for BD to distinguish from MDD.
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Affiliation(s)
- Xiao-Li Sun
- Department of Psychiatry, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Li-Na Ma
- Department of Psychiatry, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhen-Zhu Chen
- Department of Psychiatry, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan-Bing Xiong
- Department of Psychiatry, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiao Jia
- Department of Psychiatry, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Wang
- Changzhi Mental Health Center, Changzhi, China
| | - Yan Ren
- Department of Psychiatry, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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21
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Jia Y, Hui L, Sun L, Guo D, Shi M, Zhang K, Yang P, Wang Y, Liu F, Shen O, Zhu Z. Association Between Human Blood Metabolome and the Risk of Psychiatric Disorders. Schizophr Bull 2023; 49:428-443. [PMID: 36124769 PMCID: PMC10016401 DOI: 10.1093/schbul/sbac130] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
BACKGROUND AND HYPOTHESIS To identify promising drug targets for psychiatric disorders, we applied Mendelian randomization (MR) design to systematically screen blood metabolome for potential mediators of psychiatric disorders and further predict target-mediated side effects. STUDY DESIGN We selected 92 unique blood metabolites from 3 metabolome genome-wide association studies (GWASs) with totally 147 827 participants. Summary statistics for bipolar disorder (BIP), attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), major depressive disorder (MDD), schizophrenia (SCZ), panic disorder (PD), autistic spectrum disorder (ASD), and anorexia nervosa (AN) originated from the Psychiatric Genomics Consortium, involving 1 143 340 participants. Mendelian randomization (MR) analyses were conducted to estimate associations of blood metabolites with psychiatric disorders. Phenome-wide MR analysis was further performed to predict side effects mediated by metabolite-targeted interventions. RESULTS Eight metabolites were identified associated with psychiatric disorders, including five established mediators: N-acetylornithine (BIP: OR, 0.72 [95% CI, 0.66-0.79]; SCZ: OR, 0.74 [0.64-0.84]), glycine (BIP: OR, 0.62 [0.50-0.77]), docosahexaenoic acid (MDD: OR, 0.96 [0.94-0.97]), 3-Hydroxybutyrate (MDD: OR, 1.14 [1.08-1.21]), butyrylcarnitine (SCZ: OR, 1.22 [1.12-1.32]); and three novel mediators: 1-arachidonoylglycerophosphocholine (1-arachidonoyl-GPC)(BIP: OR, 0.31 [0.23-0.41]), glycoproteins (BIP: OR, 0.94 [0.92-0.97]), sphingomyelins (AN: OR, 1.12 [1.06-1.19]). Phenome-wide MR analysis showed that all identified metabolites except for N-acetylornithine and 3-Hydroxybutyrate had additional effects on nonpsychiatric diseases, while glycine, 3-Hydroxybutyrate, N-acetylornithine, and butyrylcarnitine had no adverse side effects. CONCLUSIONS This MR study identified five established and three novel mediators for psychiatric disorders. N-acetylornithine, glycine, 3-Hydroxybutyrate, and butyrylcarnitine might be promising targets against psychiatric disorders with no predicted adverse side effects.
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Affiliation(s)
- Yiming Jia
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Li Hui
- Research Center of Biological Psychiatry, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Lulu Sun
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Daoxia Guo
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
- School of Nursing, Medical College of Soochow University, Suzhou, China
| | - Mengyao Shi
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Kaixin Zhang
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Pinni Yang
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Yu Wang
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Fanghua Liu
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Ouxi Shen
- Department of Occupational Health, Suzhou Industrial Park Center for Disease Control and Prevention, Suzhou, China
| | - Zhengbao Zhu
- Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
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22
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Sato S, Yu Z, Sakai M, Motoike IN, Saigusa D, Hirayama R, Kikuchi Y, Abe T, Kinoshita K, Koshiba S, Tomita H. Decreased β-hydroxybutyrate and ketogenic amino acid levels in depressed human adults. Eur J Neurosci 2023; 57:1018-1032. [PMID: 36750311 DOI: 10.1111/ejn.15931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 01/24/2023] [Accepted: 01/29/2023] [Indexed: 02/09/2023]
Abstract
β-hydroxybutyrate (BHB) is a major ketone body synthesized mainly in the liver mitochondria and is associated with stress and severity of depression in humans. It is known to alleviate depressive-like behaviors in mouse models of depression. In this study, plasma BHB, ketogenic and glucogenic amino acids selected from the Tohoku Medical Megabank Project Community-Based Cohort Study were analysed and measured using nuclear magnetic resonance spectroscopy. The Center for Epidemiologic Studies Depression Scale (CES-D) was utilized to select adult participants with depressive symptoms (CES-D ≥ 16; n = 5722) and control participants (CES-D < 16; n = 18,150). We observed significantly reduced plasma BHB, leucine, and tryptophan levels in participants with depressive symptoms. Using social defeat stress (SDS) mice models, we found that BHB levels in mice sera increased after acute SDS, but showed no change after chronic SDS, which differed from human plasma results. Furthermore, acute SDS increased mitochondrial BHB levels in the prefrontal cortex at 6 h. In contrast, chronic SDS significantly increased the amount of food intake but reduced hepatic mitochondrial BHB levels in mice. Moreover, gene transcriptions of voltage-dependent anion-selective channel 1 (Vdac1) and monocarboxylic acid transporter 1 (Mct1), major molecules relevant to mitochondrial biogenesis and BHB transporter, significantly decreased in the liver and PFC after chronic SDS exposure. These results provide evidence that hepatic and prefrontal mitochondrial biogenesis plays an important role in BHB synthesis under chronic stress and in humans with depressive symptoms.
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Affiliation(s)
- Shiho Sato
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Zhiqian Yu
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Mai Sakai
- Department of Psychiatric Nursing, Graduate School of Health Science, Tohoku University, Sendai, Japan
| | - Ikuko N Motoike
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan.,Department of System Bioinformatics, Tohoku University Graduate School of Information Sciences, Tohoku University, Sendai, Japan
| | - Daisuke Saigusa
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan.,Department of Medical Biochemistry, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Ryo Hirayama
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Yoshie Kikuchi
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Takaaki Abe
- Department of Biomedical Engineering Regenerative and Biomedical Engineering Medical Science, Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan
| | - Kengo Kinoshita
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan.,Department of System Bioinformatics, Tohoku University Graduate School of Information Sciences, Tohoku University, Sendai, Japan
| | - Seizo Koshiba
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan.,Department of Medical Biochemistry, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Hiroaki Tomita
- Department of Psychiatry, Graduate School of Medicine, Tohoku University, Sendai, Japan.,Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan.,Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
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23
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Tornero-Costa R, Martinez-Millana A, Azzopardi-Muscat N, Lazeri L, Traver V, Novillo-Ortiz D. Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review. JMIR Ment Health 2023; 10:e42045. [PMID: 36729567 PMCID: PMC9936371 DOI: 10.2196/42045] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/02/2022] [Accepted: 11/20/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. OBJECTIVE This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. METHODS A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. RESULTS A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). CONCLUSIONS These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.
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Affiliation(s)
- Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Ledia Lazeri
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
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24
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Ho CSH, Tay GWN, Wee HN, Ching J. The Utility of Amino Acid Metabolites in the Diagnosis of Major Depressive Disorder and Correlations with Depression Severity. Int J Mol Sci 2023; 24:ijms24032231. [PMID: 36768551 PMCID: PMC9916471 DOI: 10.3390/ijms24032231] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/19/2023] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
Major depressive disorder (MDD) is a highly prevalent and disabling condition with a high disease burden. There are currently no validated biomarkers for the diagnosis and treatment of MDD. This study assessed serum amino acid metabolite changes between MDD patients and healthy controls (HCs) and their association with disease severity and diagnostic utility. In total, 70 MDD patients and 70 HCs matched in age, gender, and ethnicity were recruited for the study. For amino acid profiling, serum samples were analysed and quantified by liquid chromatography-mass spectrometry (LC-MS). Receiver-operating characteristic (ROC) curves were used to classify putative candidate biomarkers. MDD patients had significantly higher serum levels of glutamic acid, aspartic acid and glycine but lower levels of 3-Hydroxykynurenine; glutamic acid and phenylalanine levels also correlated with depression severity. Combining these four metabolites allowed for accurate discrimination of MDD patients and HCs, with 65.7% of depressed patients and 62.9% of HCs correctly classified. Glutamic acid, aspartic acid, glycine and 3-Hydroxykynurenine may serve as potential diagnostic biomarkers, whereas glutamic acid and phenylalanine may be markers for depression severity. To elucidate the association between these indicators and clinical features, it is necessary to conduct additional studies with larger sample sizes that involve a spectrum of depressive symptomatology.
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Affiliation(s)
- Cyrus Su Hui Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119074, Singapore
- Correspondence:
| | - Gabrielle Wann Nii Tay
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119074, Singapore
| | - Hai Ning Wee
- Cardiovascular and Metabolic Disorders Programme, Duke-NUS Graduate Medical School, Singapore 169857, Singapore
| | - Jianhong Ching
- Cardiovascular and Metabolic Disorders Programme, Duke-NUS Graduate Medical School, Singapore 169857, Singapore
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25
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Omori NE, Malys MK, Woo G, Mansor L. Exploring the role of ketone bodies in the diagnosis and treatment of psychiatric disorders. Front Psychiatry 2023; 14:1142682. [PMID: 37139329 PMCID: PMC10149735 DOI: 10.3389/fpsyt.2023.1142682] [Citation(s) in RCA: 4] [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: 01/12/2023] [Accepted: 03/28/2023] [Indexed: 05/05/2023] Open
Abstract
In recent times, advances in the field of metabolomics have shed greater light on the role of metabolic disturbances in neuropsychiatric conditions. The following review explores the role of ketone bodies and ketosis in both the diagnosis and treatment of three major psychiatric disorders: major depressive disorder, anxiety disorders, and schizophrenia. Distinction is made between the potential therapeutic effects of the ketogenic diet and exogenous ketone preparations, as exogenous ketones in particular offer a standardized, reproducible manner for inducing ketosis. Compelling associations between symptoms of mental distress and dysregulation in central nervous system ketone metabolism have been demonstrated in preclinical studies with putative neuroprotective effects of ketone bodies being elucidated, including effects on inflammasomes and the promotion of neurogenesis in the central nervous system. Despite emerging pre-clinical data, clinical research on ketone body effectiveness as a treatment option for psychiatric disorders remains lacking. This gap in understanding warrants further investigating, especially considering that safe and acceptable ways of inducing ketosis are readily available.
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Affiliation(s)
- Naomi Elyse Omori
- Health Via Modern Nutrition Inc. (H.V.M.N.), San Francisco, CA, United States
- *Correspondence: Naomi Elyse Omori,
| | - Mantas Kazimieras Malys
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, United Kingdom
| | - Geoffrey Woo
- Health Via Modern Nutrition Inc. (H.V.M.N.), San Francisco, CA, United States
| | - Latt Mansor
- Health Via Modern Nutrition Inc. (H.V.M.N.), San Francisco, CA, United States
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26
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Maternal Metabolites Indicative of Mental Health Status during Pregnancy. Metabolites 2022; 13:metabo13010024. [PMID: 36676949 PMCID: PMC9865687 DOI: 10.3390/metabo13010024] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/16/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
Approximately 25% of individuals report poor mental health during their pregnancy or postpartum period, which may impact fetal neurodevelopment, birth outcomes, and maternal behaviors. In the present study, maternal serum samples were collected from pregnancies at 28-32 weeks gestation from the All Our Families (Alberta, Canada) cohort and assessed using nuclear magnetic resonance spectroscopy (1H-NMR) and inductively coupled plasma-mass spectrometry (ICP-MS). Individuals with poor mental health at 34-36 weeks gestation were age-matched with mentally healthy pregnant controls. Metabolites were examined against validated self-reported mental health questionnaires for associations with depressive symptoms (Edinburgh Perinatal Depression Scale) and anxiety symptoms (Spielberger State-Trait Anxiety Inventory). 1H-NMR metabolites were identified for depression (alanine, leucine, valine, methionine, phenylalanine, glucose, lactate, 3-hydroxybutyrate, and pyruvate) and anxiety (3-hydroxybutyrate). For ICP-MS, antimony and zinc were significant for depression and anxiety, respectively. Upon false discovery rate (FDR) correction at 10%, five 1H-NMR metabolites (alanine, leucine, lactate, glucose, and phenylalanine) for depression remained significantly increased. Although results warrant further validation, the identified metabolites may serve as a predictive tool for assessing mental health during pregnancy as earlier identification has the potential to aid intervention and management of poor mental health symptomology, thus avoiding harmful consequences to both mother and offspring.
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27
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Grant CW, Wilton AR, Kaddurah-Daouk R, Skime M, Biernacka J, Mayes T, Carmody T, Wang L, Lazaridis K, Weinshilboum R, Bobo WV, Trivedi MH, Croarkin PE, Athreya AP. Network science approach elucidates integrative genomic-metabolomic signature of antidepressant response and lifetime history of attempted suicide in adults with major depressive disorder. Front Pharmacol 2022; 13:984383. [PMID: 36263124 PMCID: PMC9573988 DOI: 10.3389/fphar.2022.984383] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Individuals with major depressive disorder (MDD) and a lifetime history of attempted suicide demonstrate lower antidepressant response rates than those without a prior suicide attempt. Identifying biomarkers of antidepressant response and lifetime history of attempted suicide may help augment pharmacotherapy selection and improve the objectivity of suicide risk assessments. Towards this goal, this study sought to use network science approaches to establish a multi-omics (genomic and metabolomic) signature of antidepressant response and lifetime history of attempted suicide in adults with MDD. Methods: Single nucleotide variants (SNVs) which associated with suicide attempt(s) in the literature were identified and then integrated with a) p180-assayed metabolites collected prior to antidepressant pharmacotherapy and b) a binary measure of antidepressant response at 8 weeks of treatment using penalized regression-based networks in 245 'Pharmacogenomics Research Network Antidepressant Medication Study (PGRN-AMPS)' and 103 'Combining Medications to Enhance Depression Outcomes (CO-MED)' patients with major depressive disorder. This approach enabled characterization and comparison of biological profiles and associated antidepressant treatment outcomes of those with (N = 46) and without (N = 302) a self-reported lifetime history of suicide attempt. Results: 351 SNVs were associated with suicide attempt(s) in the literature. Intronic SNVs in the circadian genes CLOCK and ARNTL (encoding the CLOCK:BMAL1 heterodimer) were amongst the top network analysis features to differentiate patients with and without a prior suicide attempt. CLOCK and ARNTL differed in their correlations with plasma phosphatidylcholines, kynurenine, amino acids, and carnitines between groups. CLOCK and ARNTL-associated phosphatidylcholines showed a positive correlation with antidepressant response in individuals without a prior suicide attempt which was not observed in the group with a prior suicide attempt. Conclusion: Results provide evidence for a disturbance between CLOCK:BMAL1 circadian processes and circulating phosphatidylcholines, kynurenine, amino acids, and carnitines in individuals with MDD who have attempted suicide. This disturbance may provide mechanistic insights for differential antidepressant pharmacotherapy outcomes between patients with MDD with versus without a lifetime history of attempted suicide. Future investigations of CLOCK:BMAL1 metabolic regulation in the context of suicide attempts may help move towards biologically-augmented pharmacotherapy selection and stratification of suicide risk for subgroups of patients with MDD and a lifetime history of attempted suicide.
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Affiliation(s)
- Caroline W. Grant
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Angelina R. Wilton
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Department of Medicine, Duke Institute for Brain Sciences, Duke University, Durham, NC, United States
| | - Michelle Skime
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Joanna Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Taryn Mayes
- Peter O’Donnell Jr. Brain Institute and the Department of Psychiatry at the University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Thomas Carmody
- Department Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Konstantinos Lazaridis
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - William V. Bobo
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL, United States
| | - Madhukar H. Trivedi
- Peter O’Donnell Jr. Brain Institute and the Department of Psychiatry at the University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Paul E. Croarkin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Arjun P. Athreya
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
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Madison CA, Kuempel J, Albrecht GL, Hillbrick L, Jayaraman A, Safe S, Chapkin RS, Eitan S. 3,3'-Diindolylmethane and 1,4-dihydroxy-2-naphthoic acid prevent chronic mild stress induced depressive-like behaviors in female mice. J Affect Disord 2022; 309:201-210. [PMID: 35461819 PMCID: PMC9153281 DOI: 10.1016/j.jad.2022.04.106] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 01/06/2022] [Accepted: 04/14/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Current pharmaceutical treatments for depression are sometimes ineffective and may have unwanted side effects that interfere with patient compliance. This study examined the potential antidepressant-like effects of dietary- and microbial-derived aryl hydrocarbon receptor (AhR) ligands, 3,3'-diindolylmethane (DIM) and 1,4-dihydroxy-2-naphthoic acid (1,4-DHNA). METHODS Female C57BL/6 mice were subjected to unpredictable chronic mild stress (UCMS) or were unstressed. For three weeks prior to UCMS mice were fed daily with vehicle or 20 mg/kg DIM, 1,4-DHNA or AhR-inactive isomer 3,7-DHNA; another group was subjected to two weeks UCMS before ligand administration began. Mice were examined for anhedonia-like behavior as measured by the sucrose preference test. Additionally, anxiety levels of the mice were examined before UCMS and ligand administration began and at the end in the open field, light/dark, elevated plus maze, novelty-induced hypophagia, and marble burying tests. At the end of the experiment they were also examined in the Morris water maze (MWM) task. RESULTS Both DIM and 1,4-DHNA, but not 3,7-DHNA, successfully prevented and reversed UCMS-induced anhedonia-like behavior. Furthermore, both DIM and DHNA had little to no effect on anxiety levels and did not induce spatial learning deficits. LIMITATIONS Additional studies are required to determine to what degree the antidepressant-like effects of DIM and 1,4-DHNA can be attributed to their activities as AhR ligands. CONCLUSIONS Our findings indicate that dietary and microbial-derived AhR ligands may have clinical applications as potential antidepressants. Future studies are necessary to elucidate the role of AhR in depression-like states and the underlying mechanisms of action.
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Affiliation(s)
- Caitlin A Madison
- Behavioral and Cellular Neuroscience, Department of Psychological and Brain Sciences, Texas A&M University, College Station, 4235 TAMU, TX 77843, USA
| | - Jacob Kuempel
- Behavioral and Cellular Neuroscience, Department of Psychological and Brain Sciences, Texas A&M University, College Station, 4235 TAMU, TX 77843, USA
| | - Georgia Lee Albrecht
- Behavioral and Cellular Neuroscience, Department of Psychological and Brain Sciences, Texas A&M University, College Station, 4235 TAMU, TX 77843, USA
| | - Lauren Hillbrick
- Behavioral and Cellular Neuroscience, Department of Psychological and Brain Sciences, Texas A&M University, College Station, 4235 TAMU, TX 77843, USA
| | - Arul Jayaraman
- Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
| | - Stephen Safe
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, 4466 TAMU, College Station, TX 77843-4466, USA
| | - Robert S Chapkin
- Department of Nutrition, Texas A&M University, College Station, TX 77843, USA
| | - Shoshana Eitan
- Behavioral and Cellular Neuroscience, Department of Psychological and Brain Sciences, Texas A&M University, College Station, 4235 TAMU, TX 77843, USA.
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Tateishi H, Setoyama D, Kato TA, Kang D, Matsushima J, Nogami K, Mawatari S, Kojima R, Fujii Y, Sakemura Y, Shiraishi T, Imamura Y, Maekawa T, Asami T, Mizoguchi Y, Monji A. Changes in the metabolites of cerebrospinal fluid induced by rTMS in treatment-resistant depression: A pilot study. Psychiatry Res 2022; 313:114636. [PMID: 35594657 DOI: 10.1016/j.psychres.2022.114636] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 04/04/2022] [Accepted: 05/12/2022] [Indexed: 02/08/2023]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) improves depressive symptoms in treatment-resistant depression (TRD). This study aimed to analyze changes in cerebrospinal fluid (CSF) metabolites in patients with TRD after rTMS. Five patients with TRD were enrolled in a high frequency (10-Hz) rTMS study. The concentration of 72 CSF metabolites were measured at baseline and at the end of the 6-week rTMS treatment. rTMS significantly increased CSF niacinamide, kynurenine, and creatinine levels and significantly decreased CSF cystine levels, but not the levels of the other 68 CSF metabolites. This is the first CSF metabolomics study on patients with TRD who underwent rTMS.
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Affiliation(s)
- Hiroshi Tateishi
- Department of Psychiatry, Faculty of Medicine, Saga University, Nabeshima 5-1-1, Saga, 849-8501, Japan.
| | - Daiki Setoyama
- Department of Clinical Chemistry and Laboratory Medicine, Graduate School of Medical Sciences, Kyushu University, Maidashi Higashi-ku 3-1-1, Fukuoka, 812-8582, Japan
| | - Takahiro A Kato
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Maidashi Higashi-ku 3-1-1, Fukuoka, 812-8582, Japan.
| | - Dongchon Kang
- Department of Clinical Chemistry and Laboratory Medicine, Graduate School of Medical Sciences, Kyushu University, Maidashi Higashi-ku 3-1-1, Fukuoka, 812-8582, Japan
| | - Jun Matsushima
- Department of Psychiatry, Faculty of Medicine, Saga University, Nabeshima 5-1-1, Saga, 849-8501, Japan
| | - Kojiro Nogami
- Department of Psychiatry, Faculty of Medicine, Saga University, Nabeshima 5-1-1, Saga, 849-8501, Japan
| | - Seiji Mawatari
- Department of Psychiatry, Faculty of Medicine, Saga University, Nabeshima 5-1-1, Saga, 849-8501, Japan
| | - Ryohei Kojima
- Department of Psychiatry, Faculty of Medicine, Saga University, Nabeshima 5-1-1, Saga, 849-8501, Japan
| | - Yuka Fujii
- Department of Psychiatry, Faculty of Medicine, Saga University, Nabeshima 5-1-1, Saga, 849-8501, Japan
| | - Yuta Sakemura
- Department of Psychiatry, Faculty of Medicine, Saga University, Nabeshima 5-1-1, Saga, 849-8501, Japan
| | - Takumi Shiraishi
- Department of Psychiatry, Faculty of Medicine, Saga University, Nabeshima 5-1-1, Saga, 849-8501, Japan
| | - Yoshiomi Imamura
- Department of Psychiatry, Faculty of Medicine, Saga University, Nabeshima 5-1-1, Saga, 849-8501, Japan
| | - Toshihiko Maekawa
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Maidashi Higashi-ku 3-1-1, Fukuoka, 812-8582, Japan
| | - Toyoko Asami
- Department of Rehabilitation Medicine, Saga University Hospital, Nabeshima 5-1-1, Saga, 849-8501, Japan
| | - Yoshito Mizoguchi
- Department of Psychiatry, Faculty of Medicine, Saga University, Nabeshima 5-1-1, Saga, 849-8501, Japan
| | - Akira Monji
- Department of Psychiatry, Faculty of Medicine, Saga University, Nabeshima 5-1-1, Saga, 849-8501, Japan
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Saito T, Suzuki H, Kishi A. Predictive Modeling of Mental Illness Onset Using Wearable Devices and Medical Examination Data: Machine Learning Approach. Front Digit Health 2022; 4:861808. [PMID: 35493532 PMCID: PMC9046696 DOI: 10.3389/fdgth.2022.861808] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 03/08/2022] [Indexed: 11/18/2022] Open
Abstract
The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical examination data to build a predictive model that can contribute to the prevention of the onset of mental illness. This was an observational study of 4,612 subjects from the health database of society-managed health insurance in Japan provided by JMDC Inc. The inputs to the predictive model were 3-months of continuous wearable data and medical examinations within and near that period; the output was the presence or absence of mental illness over the following month, as defined by insurance claims data. The features relating to the wearable data were sleep, activity, and resting heart rate, measured by a consumer-grade wearable device (specifically, Fitbit). The predictive model was built using the XGBoost algorithm and presented an area-under-the-receiver-operating-characteristic curve of 0.712 (SD = 0.02, a repeated stratified group 10-fold cross validation). The top-ranking feature importance measure was wearable data, and its importance was higher than the blood-test values from medical examinations. Detailed verification of the model showed that predictions were made based on disrupted sleep rhythms, mild physical activity duration, alcohol use, and medical examination data on disrupted eating habits as risk factors. In summary, the predictive model showed useful accuracy for grouping the risk of mental illness onset, suggesting the potential of predictive detection, and preventive intervention using wearable devices. Sleep abnormalities in particular were detected as wearable data 3 months prior to mental illness onset, and the possibility of early intervention targeting the stabilization of sleep as an effective measure for mental illness onset was shown.
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Affiliation(s)
| | | | - Akifumi Kishi
- Graduate School of Education, The University of Tokyo, Tokyo, Japan
- *Correspondence: Akifumi Kishi
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31
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Krivosova M, Gondas E, Murin R, Dohal M, Ondrejka I, Tonhajzerova I, Hutka P, Ferencova N, Visnovcova Z, Hrtanek I, Mokry J. The Plasma Levels of 3-Hydroxybutyrate, Dityrosine, and Other Markers of Oxidative Stress and Energy Metabolism in Major Depressive Disorder. Diagnostics (Basel) 2022; 12:diagnostics12040813. [PMID: 35453861 PMCID: PMC9025710 DOI: 10.3390/diagnostics12040813] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/20/2022] [Accepted: 03/24/2022] [Indexed: 02/01/2023] Open
Abstract
Major depressive disorder (MDD) is a serious mental disease with a pathophysiology that is not yet fully clarified. An increasing number of studies show an association of MDD with energy metabolism alteration and the presence of oxidative stress. We aimed to evaluate plasma levels of 3-hydroxybutyrate (3HB), NADH, myeloperoxidase, and dityrosine (di-Tyr) in adolescent and adult patients with MDD, compare them with healthy age-matched controls, and assess the effect of antidepressant treatment during hospitalisation on these levels. In our study, plasmatic levels of 3HB were elevated in both adolescents (by 55%; p = 0.0004) and adults (by 88%; p < 0.0001) with MDD compared to controls. Levels of dityrosine were increased in MDD adults (by 19%; p = 0.0092) but not adolescents. We have not found any significant effect of antidepressants on the selected parameters during the short observation period. Our study supports the findings suggesting altered energy metabolism in MDD and demonstrates its presence independently of the age of the patients.
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Affiliation(s)
- Michaela Krivosova
- Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia; (M.K.); (N.F.); (Z.V.)
| | - Eduard Gondas
- Department of Medical Biochemistry, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia; (E.G.); (R.M.)
| | - Radovan Murin
- Department of Medical Biochemistry, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia; (E.G.); (R.M.)
| | - Matus Dohal
- Department of Pharmacology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia;
| | - Igor Ondrejka
- Psychiatric Clinic, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, University Hospital Martin, 03659 Martin, Slovakia; (I.O.); (P.H.); (I.H.)
| | - Ingrid Tonhajzerova
- Department of Physiology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia;
| | - Peter Hutka
- Psychiatric Clinic, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, University Hospital Martin, 03659 Martin, Slovakia; (I.O.); (P.H.); (I.H.)
| | - Nikola Ferencova
- Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia; (M.K.); (N.F.); (Z.V.)
| | - Zuzana Visnovcova
- Biomedical Centre Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia; (M.K.); (N.F.); (Z.V.)
| | - Igor Hrtanek
- Psychiatric Clinic, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, University Hospital Martin, 03659 Martin, Slovakia; (I.O.); (P.H.); (I.H.)
| | - Juraj Mokry
- Department of Pharmacology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, 03601 Martin, Slovakia;
- Correspondence:
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32
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F Guerreiro Costa LN, Carneiro BA, Alves GS, Lins Silva DH, Faria Guimaraes D, Souza LS, Bandeira ID, Beanes G, Miranda Scippa A, Quarantini LC. Metabolomics of Major Depressive Disorder: A Systematic Review of Clinical Studies. Cureus 2022; 14:e23009. [PMID: 35415046 PMCID: PMC8993993 DOI: 10.7759/cureus.23009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/09/2022] [Indexed: 11/24/2022] Open
Abstract
Although the understanding of the pathophysiology of major depressive disorder (MDD) has advanced greatly, this has not been translated into improved outcomes. To date, no biomarkers have been identified for the diagnosis, prognosis, and therapeutic management of MDD. Thus, we aim to review the biomarkers that are differentially expressed in MDD. A systematic review was conducted in January 2022 in the PubMed/MEDLINE, Scopus, Embase, PsycINFO, and Gale Academic OneFile databases for clinical studies published from January 2001 onward using the following terms: "Depression" OR "Depressive disorder" AND "Metabolomic." Multiple metabolites were found at altered levels in MDD, demonstrating the involvement of cellular signaling metabolites, components of the cell membrane, neurotransmitters, inflammatory and immunological mediators, hormone activators and precursors, and sleep controllers. Kynurenine and acylcarnitine were identified as consistent with depression and response to treatment. The most consistent evidence found was regarding kynurenine and acylcarnitine. Although the data obtained allow us to identify how metabolic pathways are affected in MDD, there is still not enough evidence to propose changes to current diagnostic and therapeutic actions. Some limitations are the heterogeneity of studies on metabolites, methods for detection, analyzed body fluids, and treatments used. The experiments contemplated in the review identified increased or reduced levels of metabolites, but not necessarily increased or reduced the activity of the associated pathways. The information acquired through metabolomic analyses does not specify whether the changes identified in the metabolites are a cause or a consequence of the pathology.
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Affiliation(s)
- Livia N F Guerreiro Costa
- Medicine, Laboratório de Neuropsicofarmacologia, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, BRA
- Medicine, Programa de Pós-Graduação em Medicina e Saúde, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, BRA
| | - Beatriz A Carneiro
- Medicine, Laboratório de Neuropsicofarmacologia, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, Brazil, Salvador, BRA
- Medicine, Programa de Pós-Graduação em Medicina e Saúde, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, BRA
| | - Gustavo S Alves
- Medicine, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, BRA
| | - Daniel H Lins Silva
- Medicine, Laboratório de Neuropsicofarmacologia, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, BRA
- Medicine, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, BRA
| | - Daniela Faria Guimaraes
- Medicine, Laboratório de Neuropsicofarmacologia, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, BRA
- Medicine, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, BRA
| | - Lucca S Souza
- Medicine, Laboratório de Neuropsicofarmacologia, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, BRA
- Medicine, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, BRA
| | - Igor D Bandeira
- Medicine, Laboratório de Neuropsicofarmacologia, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, BRA
- Medicine, Programa de Pós-Graduação em Medicina e Saúde, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, BRA
| | - Graziele Beanes
- Medicine, Laboratório de Neuropsicofarmacologia, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, BRA
- Medicine, Programa de Pós-Graduação em Medicina e Saúde, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, BRA
| | - Angela Miranda Scippa
- Medicine, Programa de Pós-Graduação em Medicina e Saúde, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, BRA
- Medicine, Departamento de Neurociências e Saúde Mental, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, BRA
| | - Lucas C Quarantini
- Medicine, Laboratório de Neuropsicofarmacologia, Serviço de Psiquiatria do Hospital Universitário Professor Edgard Santos, Universidade Federal da Bahia, Salvador, BRA
- Medicine, Programa de Pós-Graduação em Medicina e Saúde, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, BRA
- Medicine, Departamento de Neurociências e Saúde Mental, Faculdade de Medicina da Bahia, Universidade Federal da Bahia, Salvador, BRA
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Ferguson LB, Roberts AJ, Mayfield RD, Messing RO. Blood and brain gene expression signatures of chronic intermittent ethanol consumption in mice. PLoS Comput Biol 2022; 18:e1009800. [PMID: 35176017 PMCID: PMC8853518 DOI: 10.1371/journal.pcbi.1009800] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 01/03/2022] [Indexed: 02/03/2023] Open
Abstract
Alcohol Use Disorder (AUD) is a chronic, relapsing syndrome diagnosed by a heterogeneous set of behavioral signs and symptoms. There are no laboratory tests that provide direct objective evidence for diagnosis. Microarray and RNA-Seq technologies enable genome-wide transcriptome profiling at low costs and provide an opportunity to identify biomarkers to facilitate diagnosis, prognosis, and treatment of patients. However, access to brain tissue in living patients is not possible. Blood contains cellular and extracellular RNAs that provide disease-relevant information for some brain diseases. We hypothesized that blood gene expression profiles can be used to diagnose AUD. We profiled brain (prefrontal cortex, amygdala, and hypothalamus) and blood gene expression levels in C57BL/6J mice using RNA-seq one week after chronic intermittent ethanol (CIE) exposure, a mouse model of alcohol dependence. We found a high degree of preservation (rho range: [0.50, 0.67]) between blood and brain transcript levels. There was small overlap between blood and brain DEGs, and considerable overlap of gene networks perturbed after CIE related to cell-cell signaling (e.g., GABA and glutamate receptor signaling), immune responses (e.g., antigen presentation), and protein processing / mitochondrial functioning (e.g., ubiquitination, oxidative phosphorylation). Blood gene expression data were used to train classifiers (logistic regression, random forest, and partial least squares discriminant analysis), which were highly accurate at predicting alcohol dependence status (maximum AUC: 90.1%). These results suggest that gene expression profiles from peripheral blood samples contain a biological signature of alcohol dependence that can discriminate between CIE and Air subjects.
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Affiliation(s)
- Laura B. Ferguson
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, Texas, United States of America
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, United States of America
- Department of Neuroscience, University of Texas at Austin, Austin, Texas, United States of America
| | - Amanda J. Roberts
- Animal Models Core Facility, The Scripps Research Institute, San Diego, California, United States of America
| | - R. Dayne Mayfield
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, Texas, United States of America
- Department of Neuroscience, University of Texas at Austin, Austin, Texas, United States of America
| | - Robert O. Messing
- Waggoner Center for Alcohol and Addiction Research, University of Texas at Austin, Austin, Texas, United States of America
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, United States of America
- Department of Neuroscience, University of Texas at Austin, Austin, Texas, United States of America
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Setoyama D, Lee HY, Moon JS, Tian J, Kang YE, Lee JH, Shong M, Kang D, Yi H. Immunometabolic signatures predict recovery from thyrotoxic myopathy in patients with Graves' disease. J Cachexia Sarcopenia Muscle 2022; 13:355-367. [PMID: 34970859 PMCID: PMC8818593 DOI: 10.1002/jcsm.12889] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 10/14/2021] [Accepted: 11/22/2021] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Thyroid hormone excess induces protein energy wasting, which in turn promotes muscle weakness and bone loss in patients with Graves' disease. Although most studies have confirmed a relationship between thyrotoxicosis and muscle dysfunction, few have measured changes in plasma metabolites and immune cells during the development and recovery from thyrotoxic myopathy. The aim of this study was to identify specific plasma metabolites and T-cell subsets that predict thyrotoxic myopathy recovery in patients with Graves' disease. METHODS One hundred patients (mean age, 40.0 ± 14.2 years; 67.0% female), with newly diagnosed or relapsed Graves' disease were enrolled at the start of methimazole treatment. Handgrip strength and Five Times Sit to Stand Test performance time were measured at Weeks 0, 12, and 24. In an additional 35 patients (mean age, 38.9 ± 13.5 years; 65.7% female), plasma metabolites and immunophenotypes of peripheral blood were evaluated at Weeks 0 and 12, and the results of a short physical performance battery assessment were recorded at the same time. RESULTS In both patient groups, methimazole-induced euthyroidism was associated with improved handgrip strength and lower limb muscle function at 12 weeks. Elevated plasma metabolites including acylcarnitines were restored to normal levels at Week 12 regardless of gender, body mass index, or age (P trend <0.01). Senescent CD8+ CD28- CD57+ T-cell levels in peripheral blood were positively correlated with acylcarnitine levels (P < 0.05) and decreased during thyrotoxicosis recovery (P < 0.05). High levels of senescent CD8+ T cells at Week 0 were significantly associated with small increases in handgrip strength after 12 weeks of methimazole treatment (P < 0.05), but not statistically associated with Five Times Sit to Stand Test performance. CONCLUSIONS Restoring euthyroidism in Graves' disease patients was associated with improved skeletal muscle function and performance, while thyroid hormone-associated changes in plasma acylcarnitines levels correlated with muscle dysfunction recovery. T-cell senescence-related systemic inflammation correlated with plasma acylcarnitine levels and was also associated with small increases in handgrip strength.
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Affiliation(s)
- Daiki Setoyama
- Department of Clinical Chemistry and Laboratory MedicineKyushu University HospitalFukuokaJapan
| | - Ho Yeop Lee
- Research Center for Endocrine and Metabolic Diseases, Chungnam National University HospitalChungnam National University School of MedicineDaejeonKorea
- Department of Medical ScienceChungnam National University School of MedicineDaejeonKorea
| | - Ji Sun Moon
- Research Center for Endocrine and Metabolic Diseases, Chungnam National University HospitalChungnam National University School of MedicineDaejeonKorea
| | - Jingwen Tian
- Research Center for Endocrine and Metabolic Diseases, Chungnam National University HospitalChungnam National University School of MedicineDaejeonKorea
- Department of Medical ScienceChungnam National University School of MedicineDaejeonKorea
| | - Yea Eun Kang
- Research Center for Endocrine and Metabolic Diseases, Chungnam National University HospitalChungnam National University School of MedicineDaejeonKorea
| | - Ju Hee Lee
- Research Center for Endocrine and Metabolic Diseases, Chungnam National University HospitalChungnam National University School of MedicineDaejeonKorea
| | - Minho Shong
- Research Center for Endocrine and Metabolic Diseases, Chungnam National University HospitalChungnam National University School of MedicineDaejeonKorea
- Department of Medical ScienceChungnam National University School of MedicineDaejeonKorea
| | - Dongchon Kang
- Department of Clinical Chemistry and Laboratory MedicineKyushu University HospitalFukuokaJapan
- Department of Clinical Chemistry and Laboratory Medicine, Graduate School of Medical SciencesKyushu UniversityFukuokaJapan
| | - Hyon‐Seung Yi
- Research Center for Endocrine and Metabolic Diseases, Chungnam National University HospitalChungnam National University School of MedicineDaejeonKorea
- Department of Medical ScienceChungnam National University School of MedicineDaejeonKorea
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Onitsuka T, Hirano Y, Nemoto K, Hashimoto N, Kushima I, Koshiyama D, Koeda M, Takahashi T, Noda Y, Matsumoto J, Miura K, Nakazawa T, Hikida T, Kasai K, Ozaki N, Hashimoto R. Trends in big data analyses by multicenter collaborative translational research in psychiatry. Psychiatry Clin Neurosci 2022; 76:1-14. [PMID: 34716732 PMCID: PMC9306748 DOI: 10.1111/pcn.13311] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 10/01/2021] [Accepted: 10/17/2021] [Indexed: 12/01/2022]
Abstract
The underlying pathologies of psychiatric disorders, which cause substantial personal and social losses, remain unknown, and their elucidation is an urgent issue. To clarify the core pathological mechanisms underlying psychiatric disorders, in addition to laboratory-based research that incorporates the latest findings, it is necessary to conduct large-sample-size research and verify reproducibility. For this purpose, it is critical to conduct multicenter collaborative research across various fields, such as psychiatry, neuroscience, molecular biology, genomics, neuroimaging, cognitive science, neurophysiology, psychology, and pharmacology. Moreover, collaborative research plays an important role in the development of young researchers. In this respect, the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium and Cognitive Genetics Collaborative Research Organization (COCORO) have played important roles. In this review, we first overview the importance of multicenter collaborative research and our target psychiatric disorders. Then, we introduce research findings on the pathophysiology of psychiatric disorders from neurocognitive, neurophysiological, neuroimaging, genetic, and basic neuroscience perspectives, focusing mainly on the findings obtained by COCORO. It is our hope that multicenter collaborative research will contribute to the elucidation of the pathological basis of psychiatric disorders.
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Affiliation(s)
- Toshiaki Onitsuka
- Department of Neuroimaging Psychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Kiyotaka Nemoto
- Department of Psychiatry, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Naoki Hashimoto
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Itaru Kushima
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Medical Genomics Center, Nagoya University Hospital, Nagoya, Japan
| | - Daisuke Koshiyama
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Michihiko Koeda
- Department of Neuropsychiatry, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.,Department of Neuropsychiatry, Nippon Medical School, Tama Nagayama Hospital, Tokyo, Japan
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Yoshihiro Noda
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Junya Matsumoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Kenichiro Miura
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Takanobu Nakazawa
- Department of Bioscience, Tokyo University of Agriculture, Tokyo, Japan
| | - Takatoshi Hikida
- Laboratory for Advanced Brain Functions, Institute for Protein Research, Osaka University, Osaka, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,The International Research Center for Neurointelligence (WPI-IRCN) at The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Norio Ozaki
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
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Kouter K, Videtic Paska A. 'Omics' of suicidal behaviour: A path to personalised psychiatry. World J Psychiatry 2021; 11:774-790. [PMID: 34733641 PMCID: PMC8546767 DOI: 10.5498/wjp.v11.i10.774] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 07/16/2021] [Accepted: 08/30/2021] [Indexed: 02/06/2023] Open
Abstract
Psychiatric disorders, including suicide, are complex disorders that are affected by many different risk factors. It has been estimated that genetic factors contribute up to 50% to suicide risk. As the candidate gene approach has not identified a gene or set of genes that can be defined as biomarkers for suicidal behaviour, much is expected from cutting edge technological approaches that can interrogate several hundred, or even millions, of biomarkers at a time. These include the '-omic' approaches, such as genomics, transcriptomics, epigenomics, proteomics and metabolomics. Indeed, these have revealed new candidate biomarkers associated with suicidal behaviour. The most interesting of these have been implicated in inflammation and immune responses, which have been revealed through different study approaches, from genome-wide single nucleotide studies and the micro-RNA transcriptome, to the proteome and metabolome. However, the massive amounts of data that are generated by the '-omic' technologies demand the use of powerful computational analysis, and also specifically trained personnel. In this regard, machine learning approaches are beginning to pave the way towards personalized psychiatry.
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Affiliation(s)
- Katarina Kouter
- Faculty of Medicine, Institute of Biochemistry and Molecular Genetics, University of Ljubljana, Ljubljana SI-1000, Slovenia
| | - Alja Videtic Paska
- Faculty of Medicine, Institute of Biochemistry and Molecular Genetics, University of Ljubljana, Ljubljana SI-1000, Slovenia
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Kurokawa S, Tomizawa Y, Miyaho K, Ishii D, Takamiya A, Ishii C, Sanada K, Fukuda S, Mimura M, Kishimoto T. Fecal Microbial and Metabolomic Change during treatment course for depression: An Observational Study. J Psychiatr Res 2021; 140:45-52. [PMID: 34091346 DOI: 10.1016/j.jpsychires.2021.05.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 04/17/2021] [Accepted: 05/01/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND There is growing evidence regarding the connection between alterations in gut microbiota and their metabolites in patients with depressive disorders, suggesting a potential role in pathophysiology. Our study aimed to investigate the relationship between microbial, metabolomic features and the course of treatment for depression in a real-world clinical setting. METHODS Patients diagnosed with depressive disorders were recruited, and their stool was collected at three time points during their depression treatments. Patients were divided into three groups: non-responders, responders, and stable remitters. Gut microbiomes were analyzed using 16S rRNA gene sequencing, and gut metabolomes were analyzed by a mass spectrometry approach. Microbiomes/metabolomes were compared between groups cross-sectionally and longitudinally. RESULTS A total of 33 patients were recruited and divided into non-responders (n = 16), responders (n = 11), and stable remitters (n = 6). Non-responders presented lower alpha diversity in the Phylogenic Diversity index compared to responders during the treatment course (p = 0.003). Non-responders presented increased estimated glutamate synthesis functions by the microbiota compared to responders and stable remitters (p = 0.035). There were no specific microbiota or metabolome that differentiated the three groups. LIMITATIONS Small sample size with no healthy controls. CONCLUSIONS Our results indicate that both cross-sectional microbial features and longitudinal microbial transitions are different depending on the treatment course of depression. Controlled studies, as well as animal studies, are needed in the future to elucidate the causal relationship between microbiota and depression.
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Affiliation(s)
- Shunya Kurokawa
- Department of Neuropsychiatry, School of Medicine, Keio University, Tokyo, Japan
| | - Yoshihiro Tomizawa
- Division of Pharmacotherapeutics, Faculty of Pharmacy, Keio University, Tokyo, Japan
| | - Katsuma Miyaho
- Department of Psychiatry, Showa University School of Medicine, Tokyo, Japan
| | - Daiki Ishii
- Division of Pharmacotherapeutics, Faculty of Pharmacy, Keio University, Tokyo, Japan
| | - Akihiro Takamiya
- Department of Neuropsychiatry, School of Medicine, Keio University, Tokyo, Japan
| | - Chiharu Ishii
- Institute for Advanced Biosciences, Keio University, Yamagata, Japan
| | - Kenji Sanada
- Department of Psychiatry, Showa University School of Medicine, Tokyo, Japan
| | - Shinji Fukuda
- Institute for Advanced Biosciences, Keio University, Yamagata, Japan; Intestinal Microbiota Project, Kanagawa Institute of Industrial Science and Technology, Kanagawa, Japan; Transborder Medical Research Center, University of Tsukuba, Ibaraki, Japan
| | - Masaru Mimura
- Department of Neuropsychiatry, School of Medicine, Keio University, Tokyo, Japan
| | - Taishiro Kishimoto
- Department of Neuropsychiatry, School of Medicine, Keio University, Tokyo, Japan.
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38
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Paska AV, Kouter K. Machine learning as the new approach in understanding biomarkers of suicidal behavior. Bosn J Basic Med Sci 2021; 21:398-408. [PMID: 33485296 PMCID: PMC8292863 DOI: 10.17305/bjbms.2020.5146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 12/15/2020] [Indexed: 11/16/2022] Open
Abstract
In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable more objective and faster clinical diagnosis, implement accurate monitoring of treatment response and remission, is grave. Current technological development enables analyses of various biological marks in high throughput scale at reasonable costs, and therefore 'omic' studies are entering the psychiatry research. However, big data demands a whole new plethora of skills in data processing, before clinically useful information can be extracted. So far the classical approach to data analysis did not really contribute to identification of biomarkers in psychiatry, but the extensive amounts of data might get to a higher level, if artificial intelligence in the shape of machine learning algorithms would be applied. Not many studies on machine learning in psychiatry have been published, but we can already see from that handful of studies that the potential to build a screening portfolio of biomarkers for different psychopathologies, including suicide, exists.
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Affiliation(s)
- Alja Videtič Paska
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Katarina Kouter
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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39
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Homorogan C, Nitusca D, Enatescu V, Schubart P, Moraru C, Socaciu C, Marian C. Untargeted Plasma Metabolomic Profiling in Patients with Major Depressive Disorder Using Ultra-High Performance Liquid Chromatography Coupled with Mass Spectrometry. Metabolites 2021; 11:466. [PMID: 34357360 PMCID: PMC8306682 DOI: 10.3390/metabo11070466] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/15/2021] [Accepted: 07/16/2021] [Indexed: 12/12/2022] Open
Abstract
Major depressive disorder (MDD) is a neuropsychiatric illness with an increasing incidence and a shortfall of efficient diagnostic tools. Interview-based diagnostic tools and clinical examination often lead to misdiagnosis and inefficient systematic treatment selection. Diagnostic and treatment monitoring biomarkers are warranted for MDD. Thus, the emerging field of metabolomics is a promising tool capable of portraying the metabolic repertoire of biomolecules from biological samples in a minimally invasive fashion. Herein, we report an untargeted metabolomic profiling performed in plasma samples of 11 MDD patients, at baseline (MDD1) and at 12 weeks following antidepressant therapy with escitalopram (MDD2), and in 11 healthy controls (C), using ultra-high performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry (UHPLC-QTOF-(ESI+)-MS). We found two putative metabolites ((phosphatidylserine PS (16:0/16:1) and phosphatidic acid PA (18:1/18:0)) as having statistically significant increased levels in plasma samples of MDD1 patients compared to healthy subjects. ROC analysis revealed an AUC value of 0.876 for PS (16:0/16:1), suggesting a potential diagnostic biomarker role. In addition, PS (18:3/20:4) was significantly decreased in MDD2 group compared to MDD1, with AUC value of 0.785.
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Affiliation(s)
- Claudia Homorogan
- Doctoral School, University of Medicine and Pharmacy Victor Babes Timisoara, 300041 Timisoara, Romania;
| | - Diana Nitusca
- Department of Biochemistry, University of Medicine and Pharmacy Victor Babes Timisoara, 300041 Timisoara, Romania; (D.N.); (P.S.)
| | - Virgil Enatescu
- Discipline of Psychiatry, Department of Neurosciences, University of Medicine and Pharmacy Victor Babes Timisoara, 300041 Timisoara, Romania;
- Eduard Pamfil Psychiatric Clinic, Timisoara County Emergency Clinical Hospital, 300425 Timisoara, Romania
| | - Philip Schubart
- Department of Biochemistry, University of Medicine and Pharmacy Victor Babes Timisoara, 300041 Timisoara, Romania; (D.N.); (P.S.)
| | - Corina Moraru
- BIODIATECH, Research Center for Applied Biotechnology in Diagnosis and Molecular Therapy, 400478 Cluj-Napoca, Romania; (C.M.); (C.S.)
| | - Carmen Socaciu
- BIODIATECH, Research Center for Applied Biotechnology in Diagnosis and Molecular Therapy, 400478 Cluj-Napoca, Romania; (C.M.); (C.S.)
| | - Catalin Marian
- Department of Biochemistry, University of Medicine and Pharmacy Victor Babes Timisoara, 300041 Timisoara, Romania; (D.N.); (P.S.)
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40
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Mao Q, Tian T, Chen J, Guo X, Zhang X, Zou T. Serum Metabolic Profiling of Late-Pregnant Women With Antenatal Depressive Symptoms. Front Psychiatry 2021; 12:679451. [PMID: 34305679 PMCID: PMC8295540 DOI: 10.3389/fpsyt.2021.679451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 05/24/2021] [Indexed: 12/27/2022] Open
Abstract
Background: Antenatal depression (AD) is a major public health issue worldwide and lacks objective laboratory-based tests to support its diagnosis. Recently, small metabolic molecules have been found to play a vital role in interpreting the pathogenesis of AD. Thus, non-target metabolomics was conducted in serum. Methods: Liquid chromatography-tandem mass spectrometry-based metabolomics platforms were used to conduct serum metabolic profiling of AD and non-antenatal depression (NAD). Orthogonal partial least squares discriminant analysis, the non-parametric Mann-Whitney U test, and Benjamini-Hochberg correction were used to identify the differential metabolites between AD and NAD groups; Spearman's correlation between the key differential metabolites and Edinburgh Postnatal Depression Scale (EPDS) and the stepwise logistic regression analysis was used to identify potential biomarkers. Results: In total, 79 significant differential metabolites between AD and NAD were identified. These metabolites mainly influence amino acid metabolism and glycerophospholipid metabolism. Then, PC (16:0/16:0) and betaine were significantly positively correlated with EPDS. The simplified biomarker panel consisting of these three metabolites [betaine, PC (16:0/16:0) and succinic acid] has excellent diagnostic performance (95% confidence interval = 0.911-1.000, specificity = 95%, sensitivity = 85%) in discriminating AD and NAD. Conclusion: The results suggested that betaine, PC (16:0/16:0), and succinic acid were potential biomarker panels, which significantly correlated with depression; and it could make for developing an objective method in future to diagnose AD.
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Affiliation(s)
- Qiang Mao
- Department of Pharmacology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tian Tian
- Department of Neurology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Jing Chen
- Department of Psychiatry, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Xunyi Guo
- Department of Neurology, Yongchuan Hospital of Chongqing Medical University, Chongqing, China
| | - Xueli Zhang
- Department of Psychiatry, Linyi Mental Health Center, Linyi, China
| | - Tao Zou
- Shanghai Key Laboratory of Forensic Medicine (Academy of Forensic Science), Shanghai, China
- Department of Psychiatry, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
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Time-Course of Salivary Metabolomic Profiles during Radiation Therapy for Head and Neck Cancer. J Clin Med 2021; 10:jcm10122631. [PMID: 34203786 PMCID: PMC8232617 DOI: 10.3390/jcm10122631] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/05/2021] [Accepted: 06/10/2021] [Indexed: 12/19/2022] Open
Abstract
Oral mucositis (OM) is one of the most frequently observed adverse oral events in radiation therapy for patients with head and neck cancer. Thus, objective evaluation of OM severity is needed for early and timely intervention. Here, we analyzed the time-course of salivary metabolomic profiles during the radiation therapy. The severity of OM (National Cancer Institute (NCI) Common Terminology Criteria for Adverse Events v3.0) of nine patients with head and neck cancer was evaluated. Partial least squares regression-discriminant analysis, using samples collected before radiation therapy, showed that histidine and tyrosine highly discriminated high-grade OM from low-grade OM before the start of radiation therapy (significant difference, p = 0.048 for both metabolites). Further, the pretreatment concentrations of gamma-aminobutyric acid and 2-aminobutyric acids were higher in the high-grade OM group. Although further validations are still necessary, this study showed potentially associated metabolites with worse radiotherapy-related OM among patients with head and neck cancer.
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The changes in kynurenine metabolites induced by rTMS in treatment-resistant depression: A pilot study. J Psychiatr Res 2021; 138:194-199. [PMID: 33865168 DOI: 10.1016/j.jpsychires.2021.04.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/28/2021] [Accepted: 04/04/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation technique that is considered a valuable and promising technique for improving depressive symptoms in treatment-resistant depression (TRD). However, the exact mechanism by which rTMS ameliorates depressive symptoms remains to be clarified. OBJECTIVE The aim of the present study was to analyzed the changes in metabolites of patients with TRD in the rTMS treatment, especially focusing on the kynurenine (KYN) pathway. METHODS Thirteen participants with TRD were enrolled in a high-frequency (10 Hz) rTMS study. Cognitive function, depressive symptoms and the concentration of plasma tryptophan (TRP) metabolites were measured at baseline and at the endpoint of rTMS treatment. RESULTS rTMS treatment significantly improved depressive symptom scores and some subscales of cognitive dysfunction. The present study has demonstrated that rTMS treatment significantly increased plasma TRP levels and significantly decreased plasma serotonin levels, while plasma KYN and kynurenic acid level as well as KYN/TRP ratio remained unchanged. CONCLUSIONS This is the first metabolomic study of patients with TRD undergoing rTMS treatment. To validate the present results, it is necessary to increase the number of cases including controls, use a sample of cerebrospinal fluid, and measure blood concentration over time in the course of rTMS treatment.
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Detka J, Głombik K. Insights into a possible role of glucagon-like peptide-1 receptor agonists in the treatment of depression. Pharmacol Rep 2021; 73:1020-1032. [PMID: 34003475 PMCID: PMC8413152 DOI: 10.1007/s43440-021-00274-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/04/2021] [Accepted: 05/06/2021] [Indexed: 01/23/2023]
Abstract
Depression is a highly prevalent mood disorder and one of the major health concerns in modern society. Moreover, it is characterized by a high prevalence of coexistence with many other diseases including metabolic disorders such as type 2 diabetes mellitus (T2DM) and obesity. Currently used antidepressant drugs, which mostly target brain monoaminergic neurotransmission, have limited clinical efficacy. Although the etiology of depression has not been fully elucidated, current scientific data emphasize the role of neurotrophic factors deficiencies, disturbed homeostasis between the nervous system and the immune and endocrine systems, as well as disturbances in brain energy metabolism and dysfunctions in the gut-brain axis as important factors in the pathogenesis of this neuropsychiatric disorder. Therefore, therapeutic options that could work in a way other than classic antidepressants are being sought to increase the effectiveness of the treatment. Interestingly, glucagon-like peptide-1 receptor agonists (GLP-1RAs), used in the treatment of T2DM and obesity, are known to show pro-cognitive and neuroprotective properties, and exert modulatory effects on immune, endocrine and metabolic processes in the central nervous system. This review article discusses the potential antidepressant effects of GLP-1RAs, especially in the context of their action on the processes related to neuroprotection, inflammation, stress response, energy metabolism, gut-brain crosstalk and the stability of the gut microbiota.
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Affiliation(s)
- Jan Detka
- Laboratory of Immunoendocrinology, Department of Experimental Neuroendocrinology, Polish Academy of Sciences, Maj Institute of Pharmacology, 12 Smętna Street, 31-343, Cracow, Poland.
| | - Katarzyna Głombik
- Laboratory of Immunoendocrinology, Department of Experimental Neuroendocrinology, Polish Academy of Sciences, Maj Institute of Pharmacology, 12 Smętna Street, 31-343, Cracow, Poland
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Saito N, Itoga M, Minakawa S, Kayaba H. Serum 3-Hydroxybutyrate in Patients with Psychogenic Somatoform Symptoms May Be a Predictor of the Effectiveness of Sertraline and Venlafaxine. Int J Gen Med 2021; 14:1785-1795. [PMID: 34007205 PMCID: PMC8121269 DOI: 10.2147/ijgm.s300517] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 04/13/2021] [Indexed: 11/23/2022] Open
Abstract
Background Selective serotonin reuptake inhibitors (SSRIs) and serotonin-noradrenaline reuptake inhibitors (SNRIs) are often used to treat outpatients with psychogenic somatoform symptoms but prove ineffective in some cases. The metabolite 3-hydroxybutyrate (3HB) is currently attracting attention as a marker of the severity of depression. We investigated whether serum 3HB levels in patients with psychogenic somatoform symptoms can predict the effectiveness of sertraline and venlafaxine. Patients and Methods Physical and psychiatric problems were assessed in 132 outpatients, and symptomatic response and serum 3HB concentrations were examined before and after treatment with sertraline (50 mg/day) or venlafaxine (75 mg/day). Results In 30.3% of patients with psychogenic symptoms, serum 3HB was above the upper limit of normal (<80 μmol/L). According to multiple logistic regression analysis, only episodes of suicidal ideation showed a significant positive association with elevated 3HB (odds ratio 10.2; 95% confidence interval (CI) 2.46–42.2). The sensitivity of 3HB for the effectiveness of sertraline or venlafaxine for psychosomatic symptoms was 44.6%, but specificity was 93.9%. Hierarchical multiple logistic regression analysis identified 3HB as a better predictor of the effectiveness of medication (odds ratio 10.0; 95% CI, 2.49–40.3) than episodes of suicidal ideation. Conclusion The present findings suggest that high serum 3HB levels in patients with psychogenic somatoform symptoms may be associated with suicidal ideation and the effectiveness of sertraline and venlafaxine at low to intermediate doses. The 3HB level may be a good predictor of the effectiveness of medication. Examination of serum 3HB levels may lead to earlier and more appropriate administration of sertraline and venlafaxine.
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Affiliation(s)
- Norihiro Saito
- Department of Clinical Laboratory Medicine, Hirosaki University Graduate School of Medicine, Hirosaki City, Aomori, 036-8562, Japan
| | - Masamichi Itoga
- Department of Clinical Laboratory Medicine, Hirosaki University Graduate School of Medicine, Hirosaki City, Aomori, 036-8562, Japan
| | - Satoko Minakawa
- Department of Clinical Laboratory Medicine, Hirosaki University Graduate School of Medicine, Hirosaki City, Aomori, 036-8562, Japan
| | - Hiroyuki Kayaba
- Department of Clinical Laboratory Medicine, Hirosaki University Graduate School of Medicine, Hirosaki City, Aomori, 036-8562, Japan
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Whipp AM, Vuoksimaa E, Korhonen T, Pool R, But A, Ligthart L, Hagenbeek FA, Bartels M, Bogl LH, Pulkkinen L, Rose RJ, Boomsma DI, Kaprio J. Ketone body 3-hydroxybutyrate as a biomarker of aggression. Sci Rep 2021; 11:5813. [PMID: 33712630 PMCID: PMC7955062 DOI: 10.1038/s41598-021-84635-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 02/09/2021] [Indexed: 01/05/2023] Open
Abstract
Human aggression is a complex behaviour, the biological underpinnings of which remain poorly known. To gain insights into aggression biology, we studied relationships with aggression of 11 low-molecular-weight metabolites (amino acids, ketone bodies), processed using 1H nuclear magnetic resonance spectroscopy. We used a discovery sample of young adults and an independent adult replication sample. We studied 725 young adults from a population-based Finnish twin cohort born 1983-1987, with aggression levels rated in adolescence (ages 12, 14, 17) by multiple raters and blood plasma samples at age 22. Linear regression models specified metabolites as the response variable and aggression ratings as predictor variables, and included several potential confounders. All metabolites showed low correlations with aggression, with only one-3-hydroxybutyrate, a ketone body produced during fasting-showing significant (negative) associations with aggression. Effect sizes for different raters were generally similar in magnitude, while teacher-rated (age 12) and self-rated (age 14) aggression were both significant predictors of 3-hydroxybutyrate in multi-rater models. In an independent replication sample of 960 adults from the Netherlands Twin Register, higher aggression (self-rated) was also related to lower levels of 3-hydroxybutyrate. These exploratory epidemiologic results warrant further studies on the role of ketone metabolism in aggression.
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Affiliation(s)
- A M Whipp
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
| | - E Vuoksimaa
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - T Korhonen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - R Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
| | - A But
- Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland
| | - L Ligthart
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - F A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
| | - M Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
| | - L H Bogl
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Department of Epidemiology, Centre for Public Health, Medical University of Vienna, Vienna, Austria
| | - L Pulkkinen
- Department of Psychology, University of Jyvaskyla, Jyvaskyla, Finland
| | - R J Rose
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA
| | - D I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
| | - J Kaprio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.,Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland
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46
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Nedic Erjavec G, Sagud M, Nikolac Perkovic M, Svob Strac D, Konjevod M, Tudor L, Uzun S, Pivac N. Depression: Biological markers and treatment. Prog Neuropsychopharmacol Biol Psychiatry 2021; 105:110139. [PMID: 33068682 DOI: 10.1016/j.pnpbp.2020.110139] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/06/2020] [Accepted: 10/10/2020] [Indexed: 12/14/2022]
Abstract
Nowadays depression is considered as a systemic illness with different biological mechanisms involved in its etiology, including inflammatory response, hypothalamic-pituitary-adrenal (HPA) axis dysregulation and neurotransmitter and neurotrophic systems imbalance. Novel "omics" approaches, such as metabolomics and glycomics provide information about altered metabolic pathways and metabolites, as well as disturbances in glycosylation processes affected by or causing the development of depression. The clinical diagnosis of depression continues to be established based on the presence of the specific symptoms, but due to its heterogeneous underlying biological background, that differs according to the disease stage, there is an unmet need for treatment response biomarkers which would facilitate the process of appropriate treatment selection. This paper provides an overview of the role of major stress response system, the HPA axis, and its dysregulation in depression, possible involvement of neurotrophins, especially brain-derived neurotrophic factor, glial cell line-derived neurotrophic factor and insulin-like growth factor-1, in the development of depression. Article discusses how activated inflammation processes and increased cytokine levels, as well as disturbed neurotransmitter systems can contribute to different stages of depression and could specific metabolomic and glycomic species be considered as potential biomarkers of depression. The second part of the paper includes the most recent findings about available medical treatment of depression. The described biological factors impose an optimistic conclusion that they could represent easy obtainable biomarkers potentially predicting more personalized treatment and diagnostic options.
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Affiliation(s)
- Gordana Nedic Erjavec
- Rudjer Boskovic Institute, Division of Molecular Medicine, Bijenicka cesta 54, 10000 Zagreb, Croatia
| | - Marina Sagud
- The University of Zagreb School of Medicine, Salata 3, 10000 Zagreb, Croatia; University Hospital Center Zagreb, Department of Psychiatry, Kispaticeva 12, 10000 Zagreb, Croatia
| | - Matea Nikolac Perkovic
- Rudjer Boskovic Institute, Division of Molecular Medicine, Bijenicka cesta 54, 10000 Zagreb, Croatia
| | - Dubravka Svob Strac
- Rudjer Boskovic Institute, Division of Molecular Medicine, Bijenicka cesta 54, 10000 Zagreb, Croatia
| | - Marcela Konjevod
- Rudjer Boskovic Institute, Division of Molecular Medicine, Bijenicka cesta 54, 10000 Zagreb, Croatia
| | - Lucija Tudor
- Rudjer Boskovic Institute, Division of Molecular Medicine, Bijenicka cesta 54, 10000 Zagreb, Croatia
| | - Sandra Uzun
- University Hospital Center Zagreb, Department for Anesthesiology, Reanimatology, and Intensive Care, Kispaticeva 12, 10000 Zagreb, Croatia
| | - Nela Pivac
- Rudjer Boskovic Institute, Division of Molecular Medicine, Bijenicka cesta 54, 10000 Zagreb, Croatia.
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Nishiguchi T, Iwata M, Kajitani N, Miura A, Matsuo R, Murakami S, Nakada Y, Pu S, Shimizu Y, Tsubakino T, Yamanashi T, Shinozaki G, Tsubota J, Shirayama Y, Watanabe K, Kaneko K. Stress increases blood beta-hydroxybutyrate levels and prefrontal cortex NLRP3 activity jointly in a rodent model. Neuropsychopharmacol Rep 2021; 41:159-167. [PMID: 33609086 PMCID: PMC8340844 DOI: 10.1002/npr2.12164] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 01/25/2021] [Accepted: 02/01/2021] [Indexed: 12/28/2022] Open
Abstract
Aim This study aimed to assess the response of endogenous beta‐hydroxybutyrate to psychological stress, and its association with nucleotide‐binding domain, leucine‐rich repeat, pyrin domain‐containing 3 (NLRP3) inflammasome, and stress‐induced behavior. Methods Male C57BL/6J mice were subjected to 1‐hour restraint stress to examine changes in the endogenous beta‐hydroxybutyrate and active NLRP3 levels in the prefrontal cortex. Subsequently, we created a depression model applying 10‐day social defeat stress to the male C57BL/6J mice. Results One‐hour restraint stress rapidly increased beta‐hydroxybutyrate levels in the blood. The active NLRP3 levels in the prefrontal cortex also increased significantly. A correlation was found between the increased beta‐hydroxybutyrate levels in the blood and the active NLRP3 levels in the prefrontal cortex. The mice exposed to social defeat stress exhibited depression‐ and anxiety‐like behavioral changes in the open field, social interaction, and forced swim tests. There was a correlation between these behavioral changes and endogenous beta‐hydroxybutyrate levels. Among the social defeat model mice, those with high beta‐hydroxybutyrate levels tended to have more depression‐ and anxiety‐like behavior. Conclusions The increased blood beta‐hydroxybutyrate levels due to psychological stress correlate with the active NLRP3 levels in the prefrontal cortex, suggesting that the increased beta‐hydroxybutyrate levels due to stress may reflect a reaction to brain inflammation. In addition, mice with higher blood beta‐hydroxybutyrate levels tend to exhibit increased depression‐ and anxiety‐like behaviors; thus, an increase in blood beta‐hydroxybutyrate levels due to stress may indicate stress vulnerability. Psychological stress increased blood beta‐hydroxybutyrate (BHB) levels. Mice with higher blood BHB levels tend to exhibit increased depression‐ and anxiety‐like behaviors. The increased blood BHB levels correlate with the active NLRP3 levels in the prefrontal cortex, suggesting that increased blood BHB levels may represent stress vulnerability.![]()
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Affiliation(s)
- Tsuyoshi Nishiguchi
- Faculty of Medicine, Department of Neuropsychiatry, Tottori University, Yonago, Japan
| | - Masaaki Iwata
- Faculty of Medicine, Department of Neuropsychiatry, Tottori University, Yonago, Japan
| | - Naofumi Kajitani
- Faculty of Medicine, Department of Neuropsychiatry, Tottori University, Yonago, Japan
| | - Akihiko Miura
- Faculty of Medicine, Department of Neuropsychiatry, Tottori University, Yonago, Japan
| | - Ryoichi Matsuo
- Faculty of Medicine, Department of Neuropsychiatry, Tottori University, Yonago, Japan
| | - Shumei Murakami
- Faculty of Medicine, Department of Neuropsychiatry, Tottori University, Yonago, Japan
| | - Yumeto Nakada
- Division of Clinical Laboratory, Tottori University Hospital, Tottori, Japan
| | - Shenghong Pu
- Faculty of Medicine, Department of Neuropsychiatry, Tottori University, Yonago, Japan
| | - Yuki Shimizu
- Faculty of Medicine, Department of Neuropsychiatry, Tottori University, Yonago, Japan
| | - Tatsuya Tsubakino
- Faculty of Medicine, Department of Neuropsychiatry, Tottori University, Yonago, Japan
| | - Takehiko Yamanashi
- Faculty of Medicine, Department of Neuropsychiatry, Tottori University, Yonago, Japan.,Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Gen Shinozaki
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Jun Tsubota
- Energy Technology Laboratories, Osaka Gas Co., Ltd., Osaka, Japan
| | - Yukihiko Shirayama
- Department of Psychiatry, Teikyo University Chiba Medical Center, Ichihara, Japan
| | | | - Koichi Kaneko
- Faculty of Medicine, Department of Neuropsychiatry, Tottori University, Yonago, Japan
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48
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Skeletal Muscle Metabolomic Responses to Endurance and Resistance Training in Rats under Chronic Unpredictable Mild Stress. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18041645. [PMID: 33572176 PMCID: PMC7914905 DOI: 10.3390/ijerph18041645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/02/2021] [Accepted: 02/02/2021] [Indexed: 12/31/2022]
Abstract
The objectives of this study were to compare the antidepressant effects between endurance and resistance exercise for optimizing interventions and examine the metabolomic changes in different types of skeletal muscles in response to the exercise, using a rat model of chronic unpredictable mild stress (CUMS)-induced depression. There were 32 male Sprague-Dawley rats randomly divided into a control group (C) and 3 experimental groups: CUMS control (D), endurance exercise (E), and resistance exercise (R). Group E underwent 30 min treadmill running, and group R performed 8 rounds of ladder climbing, 5 sessions per week for 4 weeks. Body weight, sucrose preference, and open field tests were performed pre and post the intervention period for changes in depressant symptoms, and the gastrocnemius and soleus muscles were sampled after the intervention for metabolomic analysis using the 1H-NMR technique. The results showed that both types of exercise effectively improved the depression-like symptoms, and the endurance exercise appeared to have a better effect. The levels of 10 metabolites from the gastrocnemius and 13 metabolites from the soleus of group D were found to be significantly different from that of group C, and both types of exercise had a callback effect on these metabolites, indicating that a number of metabolic pathways were involved in the depression and responded to the exercise interventions.
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Voelker J, Joshi K, Daly E, Papademetriou E, Rotter D, Sheehan JJ, Kuvadia H, Liu X, Dasgupta A, Potluri R. How well do clinical and demographic characteristics predict Patient Health Questionnaire-9 scores among patients with treatment-resistant major depressive disorder in a real-world setting? Brain Behav 2021; 11:e02000. [PMID: 33403828 PMCID: PMC7882175 DOI: 10.1002/brb3.2000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 11/06/2020] [Accepted: 11/18/2020] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVES To create and validate a model to predict depression symptom severity among patients with treatment-resistant depression (TRD) using commonly recorded variables within medical claims databases. METHODS Adults with TRD (here defined as > 2 antidepressant treatments in an episode, suggestive of nonresponse) and ≥ 1 Patient Health Questionnaire (PHQ)-9 record on or after the index TRD date were identified (2013-2018) in Decision Resource Group's Real World Data Repository, which links an electronic health record database to a medical claims database. A total of 116 clinical/demographic variables were utilized as predictors of the study outcome of depression symptom severity, which was measured by PHQ-9 total score category (score: 0-9 = none to mild, 10-14 = moderate, 15-27 = moderately severe to severe). A random forest approach was applied to develop and validate the predictive model. RESULTS Among 5,356 PHQ-9 scores in the study population, the mean (standard deviation) PHQ-9 score was 10.1 (7.2). The model yielded an accuracy of 62.7%. For each predicted depression symptom severity category, the mean observed scores (8.0, 12.2, and 16.2) fell within the appropriate range. CONCLUSIONS While there is room for improvement in its accuracy, the use of a machine learning tool that predicts depression symptom severity of patients with TRD can potentially have wide population-level applications. Healthcare systems and payers can build upon this groundwork and use the variables identified and the predictive modeling approach to create an algorithm specific to their population.
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Affiliation(s)
| | - Kruti Joshi
- Janssen Scientific Affairs, LLC, Titusville, NJ, USA
| | - Ella Daly
- Janssen Research & Development, LLC, Titusville, NJ, USA
| | | | | | | | | | - Xing Liu
- SmartAnalyst, Inc, New York, NY, USA
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50
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Kubo H, Setoyama D, Watabe M, Ohgidani M, Hayakawa K, Kuwano N, Sato-Kasai M, Katsuki R, Kanba S, Kang D, Kato TA. Plasma acetylcholine and nicotinic acid are correlated with focused preference for photographed females in depressed males: an economic game study. Sci Rep 2021; 11:2199. [PMID: 33500434 PMCID: PMC7838250 DOI: 10.1038/s41598-020-75115-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 10/07/2020] [Indexed: 11/12/2022] Open
Abstract
Interpersonal difficulties are often observed in major depressive disorder (MDD), while the underlying psychological and biological mechanisms have not yet been elucidated. In the present case–control study, a PC-based trust game was conducted for 38 drug-free MDD patients and 38 healthy controls (HC). In the trust game, participants invested money in a partner (trusting behaviors), and also rated each partner’s attractiveness (preference for others). In addition, blood biomarkers including metabolites were measured. Both MDD and HC males exhibited more trusting behaviors compared to females. MDD males’ preference for ordinary-attractive partners (lay-person photographs) was lower than HC males, whereas their preference for high-attractive females (fashion-model photographs) was similar levels to HC males. This tendency in MDD males could reflect a “focused (narrowed) preference for females”. As for blood biomarker analysis, the levels of 37 metabolites including acetylcholine, AMP, GMP, nicotinic acid and tryptophan were significantly different between two groups. Interestingly, among male participants, acetylcholine and nicotinic acid were negatively correlated with the level of focused preference for photographed females. In sum, we have revealed some behavioral, psychological and biological traits of trusting behaviors and preference for others especially in MDD males. Larger studies should be conducted to validate our preliminary findings.
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Affiliation(s)
- Hiroaki Kubo
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Daiki Setoyama
- Department of Clinical Chemistry and Laboratory Medicine, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Motoki Watabe
- School of Business, Monash University Malaysia, Jalan Lagoon Selatan, 46150, Bandar Sunway, Selangor Darul Ehsan, Malaysia
| | - Masahiro Ohgidani
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Kohei Hayakawa
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Nobuki Kuwano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Mina Sato-Kasai
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Ryoko Katsuki
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Shigenobu Kanba
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Dongchon Kang
- Department of Clinical Chemistry and Laboratory Medicine, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Takahiro A Kato
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-Ku, Fukuoka, 812-8582, Japan.
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