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Chi J, Shu J, Li M, Mudappathi R, Jin Y, Lewis F, Boon A, Qin X, Liu L, Gu H. Artificial Intelligence in Metabolomics: A Current Review. Trends Analyt Chem 2024; 178:117852. [PMID: 39071116 PMCID: PMC11271759 DOI: 10.1016/j.trac.2024.117852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.
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Affiliation(s)
- Jinhua Chi
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Jingmin Shu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Ming Li
- Phoenix VA Health Care System, Phoenix, AZ 85012, USA
- University of Arizona College of Medicine, Phoenix, AZ 85004, USA
| | - Rekha Mudappathi
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Yan Jin
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Freeman Lewis
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Alexandria Boon
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Xiaoyan Qin
- College of Liberal Arts and Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Li Liu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Haiwei Gu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
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Zhang Y, Lin Y, Xiong Y, Huang J, Liu Z. An Analysis of the Intestinal Microbiome Combined with Metabolomics to Explore the Mechanism of How Jasmine Tea Improves Depression in CUMS-Treated Rats. Foods 2024; 13:2636. [PMID: 39200564 PMCID: PMC11353544 DOI: 10.3390/foods13162636] [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: 06/19/2024] [Revised: 07/25/2024] [Accepted: 08/16/2024] [Indexed: 09/02/2024] Open
Abstract
Recently, research has confirmed that jasmine tea may help improve the depressive symptoms that are associated with psychiatric disorders. Our team previously found that jasmine tea improved the depressive-like behavior that is induced by chronic unpredictable mild stress (CUMS) in Sprague Dawley (SD) rats. We hypothesized that the metabolic disorder component of depression may be related to the gut microbiota, which may be reflected in the metabolome in plasma. The influence of jasmine tea on gut microbiota composition and the association with depressive-related indexes were explored. Furthermore, the metabolites in plasma that are related to the gut microbiota were identified. SD rats were treated with control or CUMS and administrated jasmine tea for 8 weeks. The 16S rRNA gene amplicon sequencing was used to analyze the gut microbiota in feces samples, and untargeted metabolomics was used to analyze the metabolites in plasma. The results found that jasmine tea significantly ameliorated the depressive behavior induced by CUMS, significantly improved the neurotransmitter concentration (BDNF and 5-HT), and decreased the pro-inflammation levels (TNF-α and NF-κB). The intervention of jasmine tea also alleviated the dysbiosis caused by CUMS; increased the relative abundance of Bacteroides, Blautia, Clostridium, and Lactobacillus; and decreased Ruminococcus and Butyrivibrio in the CUMS-treated rats. Furthermore, the serum metabolites of the CUMS-treated rats were reversed after the jasmine tea intervention, i.e., 22 were up-regulated and 18 were down-regulated, which may have a close relationship with glycerophospholipid metabolism pathways, glycine serine and threonine metabolism pathways, and nicotinate and nicotinamide metabolism pathways. Finally, there were 30 genera of gut microbiota related to the depressive-related indexes, and 30 metabolites in the plasma had a strong predictive ability for depressive behavior. Potentially, our research implies that the intervention of jasmine tea can ameliorate the depression induced by CUMS via controlling the gut flora and the host's metabolism, which is an innovative approach for the prevention and management of depression.
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Affiliation(s)
- Yangbo Zhang
- School of Pharmacy, Shaoyang University, Shaoyang 422000, China;
- Key Laboratory of Tea Science of Ministry of Education, Hunan Agricultural University, Changsha 410128, China; (Y.L.); (Y.X.); (J.H.)
| | - Yong Lin
- Key Laboratory of Tea Science of Ministry of Education, Hunan Agricultural University, Changsha 410128, China; (Y.L.); (Y.X.); (J.H.)
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China
| | - Yifan Xiong
- Key Laboratory of Tea Science of Ministry of Education, Hunan Agricultural University, Changsha 410128, China; (Y.L.); (Y.X.); (J.H.)
| | - Jianan Huang
- Key Laboratory of Tea Science of Ministry of Education, Hunan Agricultural University, Changsha 410128, China; (Y.L.); (Y.X.); (J.H.)
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China
- Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China
| | - Zhonghua Liu
- Key Laboratory of Tea Science of Ministry of Education, Hunan Agricultural University, Changsha 410128, China; (Y.L.); (Y.X.); (J.H.)
- National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China
- Co-Innovation Center of Education Ministry for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha 410128, China
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Pasqualette L, Fidalgo TKDS, Freitas-Fernandes LB, Souza GGL, Imbiriba LA, Lobo LA, Volchan E, Domingues RMCP, Valente AP, Miranda KR. Alterations in Vagal Tone Are Associated with Changes in the Gut Microbiota of Adults with Anxiety and Depression Symptoms: Analysis of Fecal Metabolite Profiles. Metabolites 2024; 14:450. [PMID: 39195546 DOI: 10.3390/metabo14080450] [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: 07/01/2024] [Revised: 08/09/2024] [Accepted: 08/12/2024] [Indexed: 08/29/2024] Open
Abstract
Accumulating evidence suggests that interactions between the brain and gut microbiota significantly impact brain function and mental health. In the present study, we aimed to investigate whether young, healthy adults without psychiatric diagnoses exhibit differences in metabolic stool and microbiota profiles based on depression/anxiety scores and heart rate variability (HRV) parameters. Untargeted nuclear magnetic resonance-based metabolomics was used to identify fecal metabolic profiles. Results were subjected to multivariate analysis through principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), and the metabolites were identified through VIP score. Metabolites separating asymptomatic and symptomatic groups were acetate, valine, and glutamate, followed by sugar regions, glutamine, acetone, valerate, and acetoacetate. The main metabolites identified in high vagal tone (HVT) and low vagal tone (LVT) groups were acetate, valerate, and glutamate, followed by propionate and butyrate. In addition to the metabolites identified by the PLS-DA test, significant differences in aspartate, sarcosine, malate, and methionine were observed between the groups. Levels of acetoacetate were higher in both symptomatic and LVT groups. Valerate levels were significantly increased in the symptomatic group, while isovalerate, propionate, glutamate, and acetone levels were significantly increased in the LVT group. Furthermore, distinct abundance between groups was only confirmed for the Firmicutes phylum. Differences between participants with high and low vagal tone suggest that certain metabolites are involved in communication between the vagus nerve and the brain.
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Affiliation(s)
- Laura Pasqualette
- Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, Brazil
- Developmental and Educational Psychology, University of Bremen, 28359 Bremen, Germany
| | - Tatiana Kelly da Silva Fidalgo
- Pediatric Dentistry, Department of Preventive and Community Dentistry, State University of Rio de Janeiro, Rio de Janeiro 20551-030, Brazil
| | - Liana Bastos Freitas-Fernandes
- National Centre of Nuclear Magnetic Resonance/CENABIO, Medical Biochemistry, Federal University of Rio de Janeiro, Rio de Janeiro 21941-902, Brazil
| | - Gabriela Guerra Leal Souza
- Laboratory of Psychophysiology, Department of Biological Sciences, Federal University of Ouro Preto, Ouro Preto 35400-000, Brazil
| | - Luís Aureliano Imbiriba
- School of Physical Education and Sports, Federal University of Rio de Janeiro, Rio de Janeiro 21941-599, Brazil
| | - Leandro Araujo Lobo
- Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, Brazil
| | - Eliane Volchan
- Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro 21941-902, Brazil
| | | | - Ana Paula Valente
- National Centre of Nuclear Magnetic Resonance/CENABIO, Medical Biochemistry, Federal University of Rio de Janeiro, Rio de Janeiro 21941-902, Brazil
| | - Karla Rodrigues Miranda
- Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-902, Brazil
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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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Yang R, Lin Z, Cai Y, Chen N, Zhou Y, Zhang J, Hong G. Assessing the risk of prenatal depressive symptoms in Chinese women: an integrated evaluation of serum metabolome, multivitamin supplement intake, and clinical blood indicators. Front Psychiatry 2024; 14:1234461. [PMID: 38274432 PMCID: PMC10808622 DOI: 10.3389/fpsyt.2023.1234461] [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: 06/04/2023] [Accepted: 12/11/2023] [Indexed: 01/27/2024] Open
Abstract
Background Prenatal depressive symptoms (PDS) is a serious public health problem. This study aimed to develop an integrated panel and nomogram to assess at-risk populations by examining the association of PDS with the serum metabolome, multivitamin supplement intake, and clinical blood indicators. Methods This study comprised 221 pregnant women, categorized into PDS and non-PDS groups based on the Edinburgh postnatal depression scale. The participants were divided into training and test sets according to their enrollment time. We conducted logistic regression analysis to identify risk factors, and employed liquid chromatography/high resolution mass spectrometry-based serum metabolome analysis to identify metabolic biomarkers. Multiple factor analysis was used to combine risk factors, clinical blood indicators and key metabolites, and then a nomogram was developed to estimate the probability of PDS. Results We identified 36 important differential serum metabolites as PDS biomarkers, mainly involved in amino acid metabolism and lipid metabolism. Multivitamin intake works as a protective factor for PDS. The nomogram model, including multivitamin intake, HDL-C and three key metabolites (histidine, estrone and valylasparagine), exhibited an AUC of 0.855 in the training set and 0.774 in the test set, and the calibration curves showed good agreement, indicating that the model had good stability. Conclusion Our approach integrates multiple models to identify metabolic biomarkers for PDS, ensuring their robustness. Furthermore, the inclusion of dietary factors and clinical blood indicators allows for a comprehensive characterization of each participant. The analysis culminated in an intuitive nomogram based on multimodal data, displaying potential performance in initial PDS risk assessment.
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Affiliation(s)
- Rongrong Yang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China
| | - Zhenguo Lin
- Department of Clinical Medicine, Xiamen Medical College, Xiamen, China
| | - Yanhua Cai
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China
| | - Nan Chen
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China
| | - Ying Zhou
- Department of Obstetrics and Gynecology, Clinical Medical Research Center for Obstetrics and Gynecology Diseases, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Jie Zhang
- State Key Laboratory of Vaccines for Infectious Diseases, Xiang An Biomedicine Laboratory, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, National Innovation Platform for Industry-Education Integration in Vaccine Research, School of Public Health, Xiamen University, Xiamen, China
| | - Guolin Hong
- Department of Laboratory Medicine, Xiamen Key Laboratory of Genetic Testing, The First Affiliated Hospital of Xiamen University, School of Public Health, Xiamen University, Xiamen, China
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Dong Y, Zou Z, Deng P, Fan X, Li C. Circulating metabolites and depression: a bidirectional Mendelian randomization. Front Neurosci 2023; 17:1146613. [PMID: 37152596 PMCID: PMC10160621 DOI: 10.3389/fnins.2023.1146613] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/29/2023] [Indexed: 05/09/2023] Open
Abstract
Background Studies have shown an association between depression and circulating metabolites, but the causal relationship between them has not been elucidated. The purpose of this study was to elucidate the causal relationship between circulating metabolites and depression and to explore the role of circulating metabolites in depression. Methods In this study, the top single-nucleotide polymorphisms (SNPs) associated with circulating metabolites (n = 24,925) and depression (n = 322,580) were obtained based on the publicly available genome-wide association study using two-sample Mendelian randomization (MR). SNP estimates were summarized through inverse variance weighted, MR Egger, weighted median, MR pleiotropy residual sum and outlier, and "leave-one-out" methods. Results Apolipoprotein A-I (OR 0.990, 95% CI 981-0.999) and glutamine (OR 0.985, 95% CI 0.972-0.997) had protective causal effects on depression, whereas acetoacetate (OR 1.021, 95% CI 1.009-1.034), glycoproteins (OR 1.005, 95% CI 1.000-1.009), isoleucine (OR 1.013, 95% CI 1.002-1.024), and urea (OR 1.020, 95% CI 1.000-1.039) had an anti-protective effect on depression. Reversed MR showed no effect of depression on the seven circulating metabolites. Conclusion In this study, MR analysis showed that apolipoprotein A-I and glutamine had a protective effect on depression, and acetoacetate, glycoprotein, isoleucine, glucose, and urea may be risk factors for depression. Therefore, further research must be conducted to translate the findings into practice.
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Affiliation(s)
- Yankai Dong
- Department of Cardiovascular Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Zengxiao Zou
- Department of Cardiovascular Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Pin Deng
- Department of Hand and Foot Surgery, Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, China
| | - Xiaoping Fan
- Department of Cardiovascular Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- Xiaoping Fan
| | - Chunlin Li
- Department of Neurology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
- *Correspondence: Chunlin Li
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Wei J, Zhang Z, Du Y, Yang X, Zhao L, Ni P, Ni R, Gong M, Ma X. A combination of neuroimaging and plasma metabolomic analysis suggests inflammation is associated with white matter structural connectivity in major depressive disorder. J Affect Disord 2022; 318:7-15. [PMID: 36057287 DOI: 10.1016/j.jad.2022.08.108] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 05/17/2022] [Accepted: 08/26/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a common mental disorder with unknown pathophysiology. The abnormality of white matter structural connectivity and dysregulation of metabolome in MDD had been widely reported previously. Exploration of the relationship between white matter structural connectivity and plasma metabolites would be helpful for explanation of molecular mechanism for the findings from neuroimaging researches in MDD. METHODS The diffusion spectrum imaging data were collected for identification of difference of white matter structural connectivity between MDD (n = 49) and HC (n = 68). The plasma metabolite profiles were acquired by liquid chromatography-mass spectrometry analysis and clustered as co-expression modules. The correlation analysis was performed to identify structural connectivity associated metabolite. RESULTS We identified two structural connectivity related metabolite modules. One module was correlated with fractional anisotropy (FA) value between left middle temporal gyrus and left inferior temporal gyrus, which were enriched in tryptophan metabolism pathway; another module was correlated with fiber numbers (FN) between right fusiform gyrus and right inferior temporal gyrus, which was enriched in lysophosphatidylcholine (LPC), lysophosphatidylinositol (LPI) and lysophosphatidylglycerol (LPG) lipid sets. l-Kynurenine in tryptophan metabolism pathway was negatively correlated with FN between right fusiform gyrus and right inferior temporal gyrus, and LPC was positively correlated with FA value between left middle temporal gyrus and left inferior temporal gyrus in MDD. LIMITATIONS First, the sample size was relatively small. Second, the long-term effects of antidepressants were not excluded. CONCLUSION The results suggested inflammation-related mechanism was associated with white matter structural connectivity in MDD.
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Affiliation(s)
- Jinxue Wei
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Zijian Zhang
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Yue Du
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiao Yang
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Liansheng Zhao
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Peiyan Ni
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Rongjun Ni
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Meng Gong
- Laboratory of Clinical Proteomics and Metabolomics, Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China.
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Xin Z, Wei X, Jiao Q, Gou Q, Zhang Y, Peng C, Pan Q. Whole genome sequence analysis of two subspecies of Companilactobacillus Futsaii and experimental verification of drug resistance and effect on the exploratory behavior of mice based on unique gene. PLoS One 2022; 17:e0274244. [PMID: 36084068 PMCID: PMC9462788 DOI: 10.1371/journal.pone.0274244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/24/2022] [Indexed: 11/18/2022] Open
Abstract
This study characterized the whole genome of Companilactobacillus futsaii subsp. chongqingii CQ16Z1 isolated from Chongqing of China, performed genome sequence analysis with Companilactobacillus futsaii subsp. futsaii YM0097 isolated from Taiwan of China, and experimentally verified drug resistance and effect on the exploratory behavior of male C57BL/6 mice and analysis of gut microbiota and metabolomic studies. The genome of CQ16Z1 is 2.6 Mb. Sequence analysis between genomes showed that the two strains are Companilactobacillus futsaii. The unique genes of CQ16Z1 and YM0097 are 217 and 267, which account for 9% and 11% of the whole genomes, respectively. According to unique gene annotation, the results showed that genes associated with carbohydrate metabolism, environmental information processing, metabolism of cofactors and vitamins, cell wall/membrane/envelope biogenesis, phage and drug resistance are significantly different. The results of the drug resistance experiment showed that YM0097 had different degrees of resistance to 13 antibiotics, while CQ16Z1 was sensitive to more than half of them. YM0097 contains 9 prophage regions and CQ16Z1 contains 3 prophage regions. The results of the open field test showed that the time (P = 0.005; P = 0.047) and distance (P < 0.010; P = 0.046) of the central area of Y97 group and CQ group are significantly different from the control group. The results of the elevated plus maze test showed that compared with the control group, Y97 group had significant differences in the number of entries to the open arms and the percentage of open arms entry times (P = 0.004; P = 0.025), while the difference between the CQ group and the control group was not significant. YM0097 has a more obvious effect on the exploratory behavior of mice. The effects of YM0097 and CQ16Z1 on the intestinal flora of mice are also different. YM0097 may be more beneficial to the intestinal flora of the host. And LC/MS also showed that the metabolic effects of the two strains on the host are different. Finally, we believe that YM0097 is more suitable for application research as a psychobiotics.
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Affiliation(s)
- Zhao Xin
- Department of Pathogenic Biology, Chengdu Medical College, Chengdu, China
| | - Xing Wei
- Department of Clinical Laboratory, Pidu District People’s Hospital, Chengdu, China
| | - Qiuxia Jiao
- Department of Pathogenic Biology, Chengdu Medical College, Chengdu, China
| | - Qiufeng Gou
- Department of Pathogenic Biology, Chengdu Medical College, Chengdu, China
| | - Yumeng Zhang
- Department of Pathogenic Biology, Chengdu Medical College, Chengdu, China
| | - Chaoming Peng
- First Affiliated Hospital, Chengdu Medical College, Chengdu, China
- * E-mail: (CP); (QP)
| | - Qu Pan
- Department of Pathogenic Biology, Chengdu Medical College, Chengdu, China
- * E-mail: (CP); (QP)
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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: 1.5] [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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10
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Zhao H, Jin K, Jiang C, Pan F, Wu J, Luan H, Zhao Z, Chen J, Mou T, Wang Z, Lu J, Lu S, Hu S, Xu Y, Huang M. A pilot exploration of multi-omics research of gut microbiome in major depressive disorders. Transl Psychiatry 2022; 12:8. [PMID: 35013099 PMCID: PMC8748871 DOI: 10.1038/s41398-021-01769-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 11/19/2021] [Accepted: 11/30/2021] [Indexed: 12/17/2022] Open
Abstract
The pathophysiology of major depressive disorder (MDD) remains obscure. Recently, the microbiota-gut-brain (MGB) axis's role in MDD has an increasing attention. However, the specific mechanism of the multi-level effects of gut microbiota on host metabolism, immunity, and brain structure is unclear. Multi-omics approaches based on the analysis of different body fluids and tissues using a variety of analytical platforms have the potential to provide a deeper understanding of MGB axis disorders. Therefore, the data of metagenomics, metabolomic, inflammatory factors, and MRI scanning are collected from the two groups including 24 drug-naïve MDD patients and 26 healthy controls (HCs). Then, the correlation analysis is performed in all omics. The results confirmed that there are many markedly altered differences, such as elevated Actinobacteria abundance, plasma IL-1β concentration, lipid, vitamin, and carbohydrate metabolism disorder, and diminished grey matter volume (GMV) of inferior frontal gyrus (IFG) in the MDD patients. Notably, three kinds of discriminative bacteria, Ruminococcus bromii, Lactococcus chungangensis, and Streptococcus gallolyticus have an extensive correlation with metabolome, immunology, GMV, and clinical symptoms. All three microbiota are closely related to IL-1β and lipids (as an example, phosphoethanolamine (PEA)). Besides, Lactococcus chungangensis is negatively related to the GMV of left IFG. Overall, this study demonstrate that the effects of gut microbiome exert in MDD is multifactorial.
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Affiliation(s)
- Haoyang Zhao
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 31003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | - Kangyu Jin
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 31003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | - Chaonan Jiang
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 31003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | - Fen Pan
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 31003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | - Jing Wu
- The M.O.E. Key Laboratory of Laboratory Medical Diagnostics the College of Laboratory Medicine Chongqing Medical University, Chongqing, 400016, China
| | - Honglin Luan
- Department of Psychiatry, Wen Zhou seventh People's Hospital, Wenzhou, 325006, China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310027, Zhejiang Province, China
| | - Jingkai Chen
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Tingting Mou
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 31003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | - Zheng Wang
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 31003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | - Jing Lu
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 31003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | - Shaojia Lu
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 31003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | - Shaohua Hu
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 31003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | - Yi Xu
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China
- Brain Research Institute of Zhejiang University, Hangzhou, 31003, China
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China
| | - Manli Huang
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China.
- Brain Research Institute of Zhejiang University, Hangzhou, 31003, China.
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China.
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11
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Lee S, Mun S, Lee YR, Choi H, Joo EJ, Kang HG, Lee J. Discovery and validation of acetyl-L-carnitine in serum for diagnosis of major depressive disorder and remission status through metabolomic approach. Front Psychiatry 2022; 13:1002828. [PMID: 36458116 PMCID: PMC9707625 DOI: 10.3389/fpsyt.2022.1002828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/10/2022] [Indexed: 11/17/2022] Open
Abstract
Major depressive disorder (MDD) is one of the most common psychiatric disorders that accompany psychophysiological and mood changes. However, the pathophysiology-based disease mechanism of MDD is not yet fully understood, and diagnosis is also conducted through interviews with clinicians and patients. Diagnosis and treatment of MDD are limited due to the absence of biomarkers underlying the pathophysiological mechanisms of MDD. Although various attempts have been made to discover metabolite biomarkers for the diagnosis and treatment response of MDD, problems with sample size and consistency of results have limited clinical application. In addition, it was reported that future biomarker studies must consider exposure to antidepressants, which is the main cause of heterogeneity in depression subgroups. Therefore, the purpose of this study is to discover and validate biomarkers for the diagnosis of depression in consideration of exposure to drug treatment including antidepressants that contribute to the heterogeneity of the MDD subgroup. In the biomarker discovery and validation set, the disease group consisted of a mixture of patients exposed and unexposed to drug treatment including antidepressants for the treatment of MDD. The serum metabolites that differed between the MDD patients and the control group were profiled using mass spectrometry. The validation set including the remission group was used to verify the effectiveness as a biomarker for the diagnosis of depression and determination of remission status. The presence of different metabolites between the two groups was confirmed through serum metabolite profiling between the MDD patient group and the control group. Finally, Acetylcarnitine was selected as a biomarker. In validation, acetylcarnitine was significantly decreased in MDD and was distinguished from remission status. This study confirmed that the discovered acetylcarnitine has potential as a biomarker for diagnosing depression and determining remission status, regardless of exposure to drug treatment including antidepressants.
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Affiliation(s)
- Seungyeon Lee
- Department of Senior Healthcare, Graduate School, Eulji University, Gyeonggi, South Korea
| | - Sora Mun
- Department of Biomedical Laboratory Science, College of Health Sciences, Eulji University, Gyeonggi, South Korea
| | - You-Rim Lee
- Department of Senior Healthcare, Graduate School, Eulji University, Gyeonggi, South Korea
| | - Hyebin Choi
- Department of Laboratory Medicine, Korea University Anam Hospital, Seoul, South Korea
| | - Eun-Jeong Joo
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon, South Korea.,Department of Psychiatry, Uijeongbu Eulji Medical Center, Eulji University, Gyeonggi, South Korea
| | - Hee-Gyoo Kang
- Department of Senior Healthcare, Graduate School, Eulji University, Gyeonggi, South Korea.,Department of Biomedical Laboratory Science, College of Health Sciences, Eulji University, Gyeonggi, South Korea
| | - Jiyeong Lee
- Department of Biomedical Laboratory Science, College of Health Sciences, Eulji University, Gyeonggi, South Korea
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12
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Shutta KH, Balasubramanian R, Huang T, Jha SC, Zeleznik OA, Kroenke CH, Tinker LF, Smoller JW, Casanova R, Tworoger SS, Manson JE, Clish CB, Rexrode KM, Hankinson SE, Kubzansky LD. Plasma metabolomic profiles associated with chronic distress in women. Psychoneuroendocrinology 2021; 133:105420. [PMID: 34597898 PMCID: PMC8547060 DOI: 10.1016/j.psyneuen.2021.105420] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 09/10/2021] [Accepted: 09/12/2021] [Indexed: 11/19/2022]
Abstract
Several forms of chronic distress including anxiety and depression are associated with adverse cardiometabolic outcomes. Metabolic alterations may underlie these associations. Whether these forms of distress are associated with metabolic alterations even after accounting for comorbid conditions and other factors remains unclear. Using an agnostic approach, this study examines a broad range of metabolites in relation to chronic distress among women. For this cross-sectional study of chronic distress and 577 plasma metabolites, data are from different substudies within the Women's Health Initiative (WHI) and Nurses' Health Studies (NHSI, NHSII). Chronic distress was characterized by depressive symptoms and other depression indicators in the WHI and NHSII substudies, and by combined indicators of anxiety and depressive symptoms in the NHSI substudy. We used a two-phase discovery-validation framework, with WHI (N = 1317) and NHSII (N = 218) substudies in the discovery phase (identifying metabolites associated with distress) and NHSI (N = 558) substudy in the validation phase. A differential network analysis provided a systems-level assessment of metabolomic alterations under chronic distress. Analyses adjusted for potential confounders and mediators (demographics, comorbidities, medications, lifestyle factors). In the discovery phase, 46 metabolites were significantly associated with depression measures. In validation, six of these metabolites demonstrated significant associations with chronic distress after adjustment for potential confounders. Among women with high distress, we found lower gamma-aminobutyric acid (GABA), threonine, biliverdin, and serotonin and higher C16:0 ceramide and 3-methylxanthine. Our findings suggest chronic distress is associated with metabolomic alterations and provide specific targets for future study of biological pathways in chronic diseases.
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Affiliation(s)
- Katherine H Shutta
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, 010 Arnold House, 715 North Pleasant Street, Amherst, MA 01003, USA.
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, 010 Arnold House, 715 North Pleasant Street, Amherst, MA 01003, USA.
| | - Tianyi Huang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Shaili C Jha
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Candyce H Kroenke
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
| | - Lesley F Tinker
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
| | - Jordan W Smoller
- Department of Psychiatry and Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | | | - Shelley S Tworoger
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Cancer Epidemiology, Moffit Cancer Center, Tampa, FL, USA.
| | - JoAnn E Manson
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA.
| | - Kathryn M Rexrode
- Harvard Medical School, Boston, MA, USA; Division of Women's Health, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
| | - Susan E Hankinson
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, 010 Arnold House, 715 North Pleasant Street, Amherst, MA 01003, USA.
| | - Laura D Kubzansky
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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13
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Galvão ACDM, Almeida RN, de Sousa Júnior GM, Leocadio-Miguel MA, Palhano-Fontes F, de Araujo DB, Lobão-Soares B, Maia-de-Oliveira JP, Nunes EA, Hallak JEC, Sarris J, Galvão-Coelho NL. Potential biomarkers of major depression diagnosis and chronicity. PLoS One 2021; 16:e0257251. [PMID: 34587177 PMCID: PMC8480905 DOI: 10.1371/journal.pone.0257251] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/26/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Molecular biomarkers are promising tools to be routinely used in clinical psychiatry. Among psychiatric diseases, major depression disorder (MDD) has gotten attention due to its growing prevalence and morbidity. METHODS We tested some peripheral molecular parameters such as serum mature Brain-Derived Neurotrophic Factor (mBDNF), plasma C-Reactive Protein (CRP), serum cortisol (SC), and the salivary Cortisol Awakening Response (CAR), as well as the Pittsburgh sleep quality inventory (PSQI), as part of a multibiomarker panel for potential use in MDD diagnosis and evaluation of disease's chronicity using regression models, and ROC curve. RESULTS For diagnosis model, two groups were analyzed: patients in the first episode of major depression (MD: n = 30) and a healthy control (CG: n = 32). None of those diagnosis models tested had greater power than Hamilton Depression Rating Scale-6. For MDD chronicity, a group of patients with treatment-resistant major depression (TRD: n = 28) was tested across the MD group. The best chronicity model (p < 0.05) that discriminated between MD and TRD included four parameters, namely PSQI, CAR, SC, and mBDNF (AUC ROC = 0.99), with 96% of sensitivity and 93% of specificity. CONCLUSION These results indicate that changes in specific biomarkers (CAR, SC, mBDNF and PSQI) have potential on the evaluation of MDD chronicity, but not for its diagnosis. Therefore, these findings can contribute for further studies aiming the development of a stronger model to be commercially available and used in psychiatry clinical practice.
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Affiliation(s)
- Ana Cecília de Menezes Galvão
- Laboratory of Hormone Measurement, Postgraduate Program in Psychobiology and Department of Physiology and Behavior, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | - Raíssa Nobrega Almeida
- Laboratory of Hormone Measurement, Postgraduate Program in Psychobiology and Department of Physiology and Behavior, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | - Geovan Menezes de Sousa Júnior
- Laboratory of Hormone Measurement, Postgraduate Program in Psychobiology and Department of Physiology and Behavior, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | - Mário André Leocadio-Miguel
- Laboratory of Hormone Measurement, Postgraduate Program in Psychobiology and Department of Physiology and Behavior, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | | | | | - Bruno Lobão-Soares
- National Institute of Science and Technology in Translational Medicine, Ribeirao Preto, Brazil
- Department of Biophysics and Pharmacology, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | - João Paulo Maia-de-Oliveira
- National Institute of Science and Technology in Translational Medicine, Ribeirao Preto, Brazil
- Department of Clinical Medicine, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | - Emerson Arcoverde Nunes
- National Institute of Science and Technology in Translational Medicine, Ribeirao Preto, Brazil
- Department of Psychiatry, Federal University of Rio Grande do Norte, Natal, RN, Brazil
| | - Jaime Eduardo Cecilio Hallak
- National Institute of Science and Technology in Translational Medicine, Ribeirao Preto, Brazil
- Department of Neurosciences and Behavior, University of Sao Paulo (USP), Ribeirão Preto, SP, Brazil
| | - Jerome Sarris
- NICM Health Research Institute, Western Sydney University, Westmead, Australia
- Professorial Unit, Department of Psychiatry, The Melbourne Clinic, University of Melbourne, Melbourne, Australia
| | - Nicole Leite Galvão-Coelho
- Laboratory of Hormone Measurement, Postgraduate Program in Psychobiology and Department of Physiology and Behavior, Federal University of Rio Grande do Norte, Natal, RN, Brazil
- National Institute of Science and Technology in Translational Medicine, Ribeirao Preto, Brazil
- NICM Health Research Institute, Western Sydney University, Westmead, Australia
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14
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Hung CI, Lin G, Chiang MH, Chiu CY. Metabolomics-based discrimination of patients with remitted depression from healthy controls using 1H-NMR spectroscopy. Sci Rep 2021; 11:15608. [PMID: 34341439 PMCID: PMC8329159 DOI: 10.1038/s41598-021-95221-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 07/13/2021] [Indexed: 11/24/2022] Open
Abstract
The aim of the study was to investigate differences in metabolic profiles between patients with major depressive disorder (MDD) with full remission (FR) and healthy controls (HCs). A total of 119 age-matched MDD patients with FR (n = 47) and HCs (n = 72) were enrolled and randomly split into training and testing sets. A 1H-nuclear magnetic resonance (NMR) spectroscopy-based metabolomics approach was used to identify differences in expressions of plasma metabolite biomarkers. Eight metabolites, including histidine, succinic acid, proline, acetic acid, creatine, glutamine, glycine, and pyruvic acid, were significantly differentially-expressed in the MDD patients with FR in comparison with the HCs. Metabolic pathway analysis revealed that pyruvate metabolism via the tricarboxylic acid cycle linked to amino acid metabolism was significantly associated with the MDD patients with FR. An algorithm based on these metabolites employing a linear support vector machine differentiated the MDD patients with FR from the HCs with a predictive accuracy, sensitivity, and specificity of nearly 0.85. A metabolomics-based approach could effectively differentiate MDD patients with FR from HCs. Metabolomic signatures might exist long-term in MDD patients, with metabolic impacts on physical health even in patients with FR.
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Affiliation(s)
- Ching-I Hung
- Department of Psychiatry, Chang-Gung Memorial Hospital at Linkou, Taoyuan, Taiwan, ROC
- College of Medicine, Chang Gung University, Taoyuan, Taiwan, ROC
| | - Gigin Lin
- College of Medicine, Chang Gung University, Taoyuan, Taiwan, ROC
- Department of Medical Imaging and Intervention, Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan, ROC
- Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan, ROC
| | - Meng-Han Chiang
- College of Medicine, Chang Gung University, Taoyuan, Taiwan, ROC
- Department of Medical Imaging and Intervention, Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan, ROC
- Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan, ROC
| | - Chih-Yung Chiu
- College of Medicine, Chang Gung University, Taoyuan, Taiwan, ROC.
- Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan, ROC.
- Division of Pediatric Pulmonology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, 5 Fu-Shing St., Kweishan, Taoyuan, 333, Taiwan, ROC.
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15
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Gong X, Huang C, Yang X, Chen J, Pu J, He Y, Xie P. Altered Fecal Metabolites and Colonic Glycerophospholipids Were Associated With Abnormal Composition of Gut Microbiota in a Depression Model of Mice. Front Neurosci 2021; 15:701355. [PMID: 34349620 PMCID: PMC8326978 DOI: 10.3389/fnins.2021.701355] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 06/09/2021] [Indexed: 12/21/2022] Open
Abstract
The microbiota–gut–brain axis has been considered to play an important role in the development of depression, but the underlying mechanism remains unclear. The gastrointestinal tract is home to trillions of microbiota and the colon is considered an important site for the interaction between microbiota and host, but few studies have been conducted to evaluate the alterations in the colon. Accordingly, in this study, we established a chronic social defeated stress (CSDS) mice model of depression. We applied 16S rRNA gene sequencing to assess the gut microbial composition and gas and liquid chromatography–mass spectroscopy to identify fecal metabolites and colonic lipids, respectively. Meanwhile, we used Spearman’s correlation analysis method to evaluate the associations between the gut microbiota, fecal metabolites, colonic lipids, and behavioral index. In total, there were 20 bacterial taxa and 18 bacterial taxa significantly increased and decreased, respectively, in the CSDS mice. Further, microbial functional prediction demonstrated a disturbance of lipid, carbohydrate, and amino acid metabolism in the CSDS mice. We also found 20 differential fecal metabolites and 36 differential colonic lipids (in the category of glycerolipids, glycerophospholipids, and sphingolipids) in the CSDS mice. Moreover, correlation analysis showed that fecal metabolomic signature was associated with the alterations in the gut microbiota composition and colonic lipidomic profile. Of note, three lipids [PC(16:0/20:4), PG(22:6/22:6), and PI(18:0/20:3), all in the category of glycerophospholipids] were significantly associated with anxiety- and depression-like phenotypes in mice. Taken together, our results indicated that the gut microbiota might be involved in the pathogenesis of depression via influencing fecal metabolites and colonic glycerophospholipid metabolism.
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Affiliation(s)
- Xue Gong
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Chongqing Key Laboratory of Neurobiology, Chongqing, China
| | - Cheng Huang
- Department of Neurology and Stroke Center, The First Affiliated Hospital of Jinan University, Clinical Neuroscience Institute of Jinan University, Guangzhou, China
| | - Xun Yang
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jianjun Chen
- Institute of Life Sciences, Chongqing Medical University, Chongqing, China
| | - Juncai Pu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Chongqing Key Laboratory of Neurobiology, Chongqing, China
| | - Yong He
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Chongqing Key Laboratory of Neurobiology, Chongqing, China
| | - Peng Xie
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.,Chongqing Key Laboratory of Neurobiology, Chongqing, China
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16
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Gao Y, Li X, Zhao HL, Ling-Hu T, Zhou YZ, Tian JS, Qin XM. Comprehensive Analysis Strategy of Nervous-Endocrine-Immune-Related Metabolites to Evaluate Arachidonic Acid as a Novel Diagnostic Biomarker in Depression. J Proteome Res 2021; 20:2477-2486. [PMID: 33797260 DOI: 10.1021/acs.jproteome.0c00940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Depression is one of the most complex multifactorial diseases affected by genetic and environmental factors. The molecular mechanism underlying depression remains largely unclear. To address this issue, a novel nervous-endocrine-immune (NEI) network module was used to find the metabolites and evaluate the diagnostic ability of patients with depression. During this process, metabolites were acquired from a professional depression metabolism database. Over-representation analysis was performed using IMPaLA. Then, the metabolite-metabolite interaction (MMI) network of the NEI system was used to select key metabolites. Finally, the receiver operating characteristic curve analysis was evaluated for the diagnostic ability of arachidonic acid. The results show that the numbers of the nervous system, endocrine system, and immune system pathways are 10, 19, and 12 and the numbers of metabolites are 38, 52, and 13, respectively. The selected shared metabolite-enriched pathways can be 97.56% of the NEI-related pathways. Arachidonic acid was extracted from the NEI system network by using an optimization formula and validated by in vivo experiments. It was indicated that the proposed model was good at screening arachidonic acid for the diagnosis of depression. This method provides reliable evidences and references for the diagnosis and mechanism research of other related diseases.
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Affiliation(s)
- Yao Gao
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan 030006 Shanxi, China.,Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Shanxi University, Taiyuan 030006 Shanxi, China
| | - Xiao Li
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan 030006 Shanxi, China.,Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Shanxi University, Taiyuan 030006 Shanxi, China
| | - Hui-Liang Zhao
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan 030006 Shanxi, China.,Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Shanxi University, Taiyuan 030006 Shanxi, China
| | - Ting Ling-Hu
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan 030006 Shanxi, China.,Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Shanxi University, Taiyuan 030006 Shanxi, China
| | - Yu-Zhi Zhou
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan 030006 Shanxi, China.,Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Shanxi University, Taiyuan 030006 Shanxi, China
| | - Jun-Sheng Tian
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan 030006 Shanxi, China.,Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Shanxi University, Taiyuan 030006 Shanxi, China
| | - Xue-Mei Qin
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan 030006 Shanxi, China.,Shanxi Key Laboratory of Active Constituents Research and Utilization of TCM, Shanxi University, Taiyuan 030006 Shanxi, China
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17
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Nobis A, Zalewski D, Waszkiewicz N. Peripheral Markers of Depression. J Clin Med 2020; 9:E3793. [PMID: 33255237 PMCID: PMC7760788 DOI: 10.3390/jcm9123793] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/09/2020] [Accepted: 11/19/2020] [Indexed: 12/22/2022] Open
Abstract
Major Depressive Disorder (MDD) is a leading cause of disability worldwide, creating a high medical and socioeconomic burden. There is a growing interest in the biological underpinnings of depression, which are reflected by altered levels of biological markers. Among others, enhanced inflammation has been reported in MDD, as reflected by increased concentrations of inflammatory markers-C-reactive protein, interleukin-6, tumor necrosis factor-α and soluble interleukin-2 receptor. Oxidative and nitrosative stress also plays a role in the pathophysiology of MDD. Notably, increased levels of lipid peroxidation markers are characteristic of MDD. Dysregulation of the stress axis, along with increased cortisol levels, have also been reported in MDD. Alterations in growth factors, with a significant decrease in brain-derived neurotrophic factor and an increase in fibroblast growth factor-2 and insulin-like growth factor-1 concentrations have also been found in MDD. Finally, kynurenine metabolites, increased glutamate and decreased total cholesterol also hold promise as reliable biomarkers for MDD. Research in the field of MDD biomarkers is hindered by insufficient understanding of MDD etiopathogenesis, substantial heterogeneity of the disorder, common co-morbidities and low specificity of biomarkers. The construction of biomarker panels and their evaluation with use of new technologies may have the potential to overcome the above mentioned obstacles.
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Affiliation(s)
- Aleksander Nobis
- Department of Psychiatry, Medical University of Bialystok, pl. Brodowicza 1, 16-070 Choroszcz, Poland; (D.Z.); (N.W.)
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18
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Liu S. Metabonomic Profile and Signaling Pathway Prediction of Depression-Associated Suicidal Behavior. Front Psychiatry 2020; 11:269. [PMID: 32372980 PMCID: PMC7177018 DOI: 10.3389/fpsyt.2020.00269] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 03/19/2020] [Indexed: 11/13/2022] Open
Abstract
Suicide is the most severe consequence of depression which has become a leading cause of disability and a global disease burden. Recent evidence indicates a central role of small molecules in the pathogenesis of depression and associated suicidal behaviors. However, there lacks a systemic exploration of small molecules in the development of depression-associated suicide, and it remains unclear how they affect an individual's behavior. In order to compare the metabonomic profiles between drug-naïve patients with depression-associated suicidal behaviors and healthy individuals, we conducted a systemic database search for studies of metabolic characteristics in depression-associated suicidal behavior. Manual data curation and statistical analysis and integration were performed in Excel. We further performed an enrichment analysis of signaling pathway prediction using the Reactome Pathway Analysis tool. We have identified 17 metabolites that expressed differently between drug-naïve patients with depression-associated suicidal behaviors and healthy controls. We have integrated these metabolites into biological signaling pathways and provided a visualized signaling network in depressed suicidal patients. We have revealed that "transport of small molecules", "disease", "metabolism" and "metabolism of proteins" were the most relevant signaling sections, among which "transport of inorganic cations/anions and amino acids/oligopeptides", "SLC-mediated transmembrane transport", and "metabolism of amino acids and derivatives" should be further studied to elucidate their potential pathogenic mechanism in the development of depression and associated suicidal behavior. In conclusion, our findings of these 17 metabolites and associated signaling pathways could provide an insight into the molecular pathogenesis of depression-associated suicidal behavior and potential targets for new drug inventions.
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Affiliation(s)
- Song Liu
- Department of General Surgery, Nanjing Drum Tower Hospital, Nanjing, China
- Medical School of Nanjing University, Nanjing, China
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19
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Esposito CM, Buoli M. The biological face of melancholia: Are there any reliable biomarkers for this depression subtype? J Affect Disord 2020; 266:802-809. [PMID: 32217262 DOI: 10.1016/j.jad.2020.02.036] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 01/24/2020] [Accepted: 02/18/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND Melancholic depression (MD) is a subtype of Major Depression associated with more clinical severity and poorer prognosis that non-melancholic depression (NMD). The differentiation between depression subtypes is still clinical, although the identification of specific biomarkers could be useful for diagnosis and the development of new treatments. Purpose of the present manuscript is to review the biomarkers that have been associated with MD. METHODS We performed a bibliographic research on the main databases (PubMed, Embase, PsycInfo, Isi Web of Knowledge, Medscape, The Cochrane Library), in order to find studies that proposed biological markers for melancholic depression. A total of 14 studies met our inclusion criteria. RESULTS Most of studies focused on immune dysregulation. Subjects with MD show biological abnormalities than healthy controls (HC). MD might be characterized by specific biological changes and it could be associated to more severe abnormalities with respect to NMD; however especially about this latter point the available data are preliminary. LIMITATIONS Most available data have not been replicated; the studies focused on different biomarkers. In addition, many articles report results on a limited sample size. CONCLUSIONS Melancholic depression is a subtype of major depression that seems to be associated with specific alterations of different biological systems. Future studies with larger sample can confirm the results and hypothesis presented in this review.
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Affiliation(s)
- Cecilia Maria Esposito
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, Milan 20122, Italy.
| | - Massimiliano Buoli
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Via F. Sforza 35, Milan 20122, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
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20
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Battineni G, Sagaro GG, Chinatalapudi N, Amenta F. Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis. J Pers Med 2020; 10:jpm10020021. [PMID: 32244292 PMCID: PMC7354442 DOI: 10.3390/jpm10020021] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/09/2020] [Accepted: 03/23/2020] [Indexed: 02/07/2023] Open
Abstract
This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.
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Affiliation(s)
- Gopi Battineni
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
- Correspondence: ; Tel.: +39-333-172-8206
| | - Getu Gamo Sagaro
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
| | - Nalini Chinatalapudi
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
| | - Francesco Amenta
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
- Research Department, International Medical Radio Center Foundation (C.I.R.M.), 00144 Roma, Italy
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21
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Humer E, Probst T, Pieh C. Metabolomics in Psychiatric Disorders: What We Learn from Animal Models. Metabolites 2020; 10:E72. [PMID: 32079262 PMCID: PMC7074444 DOI: 10.3390/metabo10020072] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 01/29/2020] [Accepted: 02/10/2020] [Indexed: 02/06/2023] Open
Abstract
Biomarkers are a recent research target within biological factors of psychiatric disorders. There is growing evidence for deriving biomarkers within psychiatric disorders in serum or urine samples in humans, however, few studies have investigated this differentiation in brain or cerebral fluid samples in psychiatric disorders. As brain samples from humans are only available at autopsy, animal models are commonly applied to determine the pathogenesis of psychiatric diseases and to test treatment strategies. The aim of this review is to summarize studies on biomarkers in animal models for psychiatric disorders. For depression, anxiety and addiction disorders studies, biomarkers in animal brains are available. Furthermore, several studies have investigated psychiatric medication, e.g., antipsychotics, antidepressants, or mood stabilizers, in animals. The most notable changes in biomarkers in depressed animal models were related to the glutamate-γ-aminobutyric acid-glutamine-cycle. In anxiety models, alterations in amino acid and energy metabolism (i.e., mitochondrial regulation) were observed. Addicted animals showed several biomarkers according to the induced drugs. In summary, animal models provide some direct insights into the cellular metabolites that are produced during psychiatric processes. In addition, the influence on biomarkers due to short- or long-term medication is a noticeable finding. Further studies should combine representative animal models and human studies on cerebral fluid to improve insight into mental disorders and advance the development of novel treatment strategies.
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Affiliation(s)
- Elke Humer
- Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, 3500 Krems, Austria; (T.P.); (C.P.)
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22
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Mitro SD, Larrabure-Torrealva GT, Sanchez SE, Molsberry SA, Williams MA, Clish C, Gelaye B. Metabolomic markers of antepartum depression and suicidal ideation. J Affect Disord 2020; 262:422-428. [PMID: 31744743 PMCID: PMC6917910 DOI: 10.1016/j.jad.2019.11.061] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 09/21/2019] [Accepted: 11/10/2019] [Indexed: 02/09/2023]
Abstract
BACKGROUND Recent analyses have described metabolomic markers for depression and suicidal ideation in non-pregnant adults. We examined the metabolomic profile of antepartum depression and suicidal ideation during mid-pregnancy, a time of high susceptibility to mood disorders. METHODS We collected fasting blood from 100 pregnant Peruvian women and profiled 307 plasma metabolites using liquid chromatography-mass spectrometry. We used the Patient Health Questionnaire 9 to define antepartum depression (score ≥ 10) and suicidal ideation (having thoughts that you would be better off dead, or of hurting yourself). Logistic regression was used to calculate odds ratios (ORs). RESULTS Three triacylglycerol metabolites (C48:5 triacylglycerol [OR = =1.89; 95% confidence interval (CI): 1.14-3.14], C50:6 triacylglycerol [OR = =1.88; 95%CI: 1.13-3.14], C46:4 triacylglycerol [OR = =1.89; 95%CI: 1.11-3.21]) were associated with higher odds of antepartum depression and 4 metabolites (betaine [OR = =0.56; 95%CI:0.33-0.95], citrulline [OR = =0.58; 95%CI: 0.34-0.98], C5 carnitine [OR = =0.59; 95%CI: 0.36-0.99], C5:1 carnitine [OR = =0.59; 95%CI: 0.35-1.00]) with lower odds of antepartum depression. Twenty-six metabolites, including 5-hydroxytryptophan (OR = =0.52; 95%CI: 0.30-0.92), phenylalanine (OR = =0.41; 95%CI: 0.19-0.91), and betaine (OR = =0.53; 95%CI: 0.28-0.99) were associated with lower odds of suicidal ideation. LIMITATIONS Our cross-sectional study could not determine whether metabolites prospectively predict outcomes. No metabolites remained significant after multiple testing correction; these novel findings should be replicated in a larger sample. CONCLUSIONS Antepartum suicidal ideation metabolomic markers are similar to markers of depression among non-pregnant adults, and distinct from markers of antepartum depression. Findings suggest that mood disorder in pregnancy shares metabolomic similarities to mood disorder at other times and may further understanding of these conditions' pathophysiology.
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Affiliation(s)
- Susanna D. Mitro
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Corresponding author: Susanna D. Mitro, Kresge Building, 9th floor, 677 Huntington Ave, Boston, MA 02115, , Tel: 513-532-6977
| | | | - Sixto E. Sanchez
- Asociación Civil PROESA, Lima, Peru,Universidad Peruana de Ciencias Aplicadas, Lima, Peru
| | - Samantha A. Molsberry
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Michelle A. Williams
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Clary Clish
- Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Bizu Gelaye
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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23
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Xu S, Liu Y, Pu J, Gui S, Zhong X, Tian L, Song X, Qi X, Wang H, Xie P. Chronic Stress in a Rat Model of Depression Disturbs the Glutamine-Glutamate-GABA Cycle in the Striatum, Hippocampus, and Cerebellum. Neuropsychiatr Dis Treat 2020; 16:557-570. [PMID: 32158215 PMCID: PMC7047974 DOI: 10.2147/ndt.s245282] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 02/17/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Major depressive disorder (MDD) is a complex psychiatric illness involving multiple brain regions. Increasing evidence indicates that the striatum is involved in depression, but the molecular mechanisms remain unclear. METHODS In this study, we performed a gas chromatography-mass spectrometer (GC/MS)-based metabolomic analysis in the striatum of depressed rats induced by chronic unpredictable mild stress (CUMS). We then compared striatal data with our previous data from the hippocampus and cerebellum to systematically investigate the potential pathogenesis of depression. RESULTS We identified 22 differential metabolites in the striatum between the CUMS and control groups; these altered metabolites were mainly involved in amino acid, carbohydrate, and nucleotide metabolism. Pathway analysis revealed that the shared metabolic pathways of the striatum, hippocampus, and cerebellum were mainly involved in the glutamine-glutamate metabolic system. Four genes in the striatum (GS, GLS2, GLT1, and SSADH), six genes in the hippocampus (GS, SNAT1, GAD1, SSADH, VGAT, and ABAT), and five genes in the cerebellum (GS, ABAT, SNAT1, VGAT, and GDH) were found to be significantly altered using RT-qPCR. Correlation analysis indicated that these differential genes were strongly correlated. CONCLUSION These results suggest that chronic stress might induce depressive behaviors by disturbing the glutamine-glutamate-GABA cycle in the striatum, hippocampus, and cerebellum, and that the glutamine-glutamate-GABA cycle among these three brain regions might generate cooperative action in response to chronic stress.
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Affiliation(s)
- Shaohua Xu
- Department of Neurology, Yongchuan Hospital, Chongqing Medical University, Chongqing 402160, People's Republic of China.,NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.,Chongqing Key Laboratory of Neurobiology, Chongqing 400016, People's Republic of China
| | - Yiyun Liu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.,Chongqing Key Laboratory of Neurobiology, Chongqing 400016, People's Republic of China
| | - Juncai Pu
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.,Chongqing Key Laboratory of Neurobiology, Chongqing 400016, People's Republic of China
| | - Siwen Gui
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.,Chongqing Key Laboratory of Neurobiology, Chongqing 400016, People's Republic of China.,College of Biomedical Engineering, Chongqing Medical University, Chongqing 400016, People's Republic of China
| | - Xiaogang Zhong
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.,Chongqing Key Laboratory of Neurobiology, Chongqing 400016, People's Republic of China
| | - Lu Tian
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.,Chongqing Key Laboratory of Neurobiology, Chongqing 400016, People's Republic of China
| | - Xuemian Song
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.,Chongqing Key Laboratory of Neurobiology, Chongqing 400016, People's Republic of China.,College of Biomedical Engineering, Chongqing Medical University, Chongqing 400016, People's Republic of China
| | - Xunzhong Qi
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.,Chongqing Key Laboratory of Neurobiology, Chongqing 400016, People's Republic of China
| | - Haiyang Wang
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.,Chongqing Key Laboratory of Neurobiology, Chongqing 400016, People's Republic of China
| | - Peng Xie
- Department of Neurology, Yongchuan Hospital, Chongqing Medical University, Chongqing 402160, People's Republic of China.,NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China.,Chongqing Key Laboratory of Neurobiology, Chongqing 400016, People's Republic of China.,Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
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24
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Lazary J, Eszlari N, Juhasz G, Bagdy G. A functional variant of CB2 receptor gene interacts with childhood trauma and FAAH gene on anxious and depressive phenotypes. J Affect Disord 2019; 257:716-722. [PMID: 31382124 DOI: 10.1016/j.jad.2019.07.083] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2019] [Revised: 07/10/2019] [Accepted: 07/29/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND Accumulating data suggest that CB2 receptor plays a crucial role in development of anxiety via regulatory function of stress response and neuroimmune crosstalk. Although animal experiments confirm this relationship, relevant human genetic studies on CB2 receptor gene (CNR2) in association with affective phenotype are absent. METHODS CNR2 R63Q and FAAH C385A functional polymorphisms were genotyped of 921 volunteers from the general population. Phenotypic variables were measured by the Zung Self-related Depression Scale (ZSDS), The State-Trait Anxiety Inventory (Trait subscale, STAI-T) and the depressive and anxious subscales of the Brief Symptom Inventory (BSI-DEP and BSI-ANX). Early life trauma was assesssed by the Childhood Trauma Questionnaire (CHQ). Using general linear models we tested possible associations between phenotypic variance and genotype distribution. RESULTS There was a significant main effect of RR genotype of R63Q on ZSDS score (p = 0.007) and a remarkble interacting effect of CHQ and R63Q on scores of ZSDS, STAI-T and BSI-ANX scales (p = 0.009; p = 0.003; p = 0.001; respectively). R allele of R63Q and A allele of FAAH C385A were associated with significantly higher ZSDS, STAI-T and BSI-ANX scores compared to non-risk allele carriers (p = 0.009; p = 0.007; p = 0.007, respectively). The highest phenotypic scores were observed in GxGxE model (pZSDS = 0.04; pBSI-DEP = 0.006; pSTAI-T = 0.001; pBSI-ANX = 3.8 × 10-5). CONCLUSIONS In this first human genetic study on CNR2 and childhood trauma we revealed that dysfunctional CB2 receptor and FAAH can contribute to greater sensitivity for childhood trauma possibly via weaker inhibiton of inflammatory and overactivated HPA axis.
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Affiliation(s)
- Judit Lazary
- Nyírő Gyula National Institute of Psychiatry and Addictions, Budapest, Hungary; MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Science, Semmelweis University, Budapest, Hungary.
| | - Nora Eszlari
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Budapest, Hungary
| | - Gabriella Juhasz
- MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Science, Semmelweis University, Budapest, Hungary; Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Budapest, Hungary; SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Brain Research Program, Budapest, Hungary; Neuroscience and Psychiatry Unit, Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom; Manchester Academic Health Sciences Centre, Manchester, United Kingdom
| | - Gyorgy Bagdy
- MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Science, Semmelweis University, Budapest, Hungary; Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Budapest, Hungary
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25
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Hadrévi J, Jonsdottir IH, Jansson PA, Eriksson JW, Sjörs A. Plasma metabolomic patterns in patients with exhaustion disorder. Stress 2019; 22:17-26. [PMID: 30084722 DOI: 10.1080/10253890.2018.1494150] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
Abstract
Exhaustion disorder (ED) is a stress-related disorder that often implies a great burden on the individual patient as well as on society. Previous studies have shown that ED is associated with metabolic deviations, such as lowered fasting glucose. Several mechanisms have been discussed as a plausible explanation of the lack of energy described by these patients. Metabolic processes and reduced ability to mobilize energy have been suggested as important factors. This study investigated metabolomics in 20 patients diagnosed with ED and compared them with 21 healthy controls. Plasma metabolic profiles were examined in both fasting and nonfasting (postprandial) conditions. Blood plasma samples were analyzed for metabolite content using gas chromatography mass spectrometry. A total of 62 different metabolites were simultaneously detected in each of the samples. Multivariate models indicated systematic differences between patients with ED and healthy controls in both their fasting and nonfasting plasma metabolite levels. Lysine and octadecenoic acid were more abundant and glutamine, glycine, serine and gluconic acid were less abundant in the patients across both conditions. In the present study, we comprehensively and simultaneously screen for changes in a large number of metabolites. Our results show a difference in systemic metabolites between patients with exhaustion disorder and healthy controls both in the fasting and in the postprandial states. Here, we present new potential biomarkers mirroring exhaustion disorder metabolism. Lay summary Exhaustion disorder (ED) patients suffer from stress-related symptoms including a reduced energy level. This study investigates the body's metabolism in patients with ED, both fasting and after a meal. New potential markers that may help future investigations on ED were identified.
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Affiliation(s)
- Jenny Hadrévi
- a Occupational and Environmental Medicine, Department of Public Health and Clinical Medicines , Umeå University , Sweden
| | - Ingibjörg H Jonsdottir
- b The Institute of Stress Medicine , Gothenburg , Sweden Region Västra Götaland
- c Department of Food and Nutrition, and Sport Science , University of Gothenburg , Gothenburg , Sweden
| | - Per-Anders Jansson
- d Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy , University of Gothenburg , Gothenburg , Sweden
| | - Jan W Eriksson
- e Department of Medical Sciences , Uppsala University , Uppsala , Sweden
| | - Anna Sjörs
- b The Institute of Stress Medicine , Gothenburg , Sweden Region Västra Götaland
- f Department of Clinical Neuroscience and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy , University of Gothenburg , Gothenburg , Sweden
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26
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Rockel JS, Kapoor M. The Metabolome and Osteoarthritis: Possible Contributions to Symptoms and Pathology. Metabolites 2018; 8:metabo8040092. [PMID: 30551581 PMCID: PMC6315757 DOI: 10.3390/metabo8040092] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 12/07/2018] [Accepted: 12/10/2018] [Indexed: 12/22/2022] Open
Abstract
Osteoarthritis (OA) is a progressive, deteriorative disease of articular joints. Although traditionally viewed as a local pathology, biomarker exploration has shown that systemic changes can be observed. These include changes to cytokines, microRNAs, and more recently, metabolites. The metabolome is the set of metabolites within a biological sample and includes circulating amino acids, lipids, and sugar moieties. Recent studies suggest that metabolites in the synovial fluid and blood could be used as biomarkers for OA incidence, prognosis, and response to therapy. However, based on clinical, demographic, and anthropometric factors, the local synovial joint and circulating metabolomes may be patient specific, with select subsets of metabolites contributing to OA disease. This review explores the contribution of the local and systemic metabolite changes to OA, and their potential impact on OA symptoms and disease pathogenesis.
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Affiliation(s)
- Jason S Rockel
- Arthritis Program, University Health Network, Toronto, ON M5T 2S8, Canada.
- Division of Genetics and Development, Krembil Research Institute, University Health Network, Toronto, ON M5T 2S8, Canada.
| | - Mohit Kapoor
- Arthritis Program, University Health Network, Toronto, ON M5T 2S8, Canada.
- Division of Genetics and Development, Krembil Research Institute, University Health Network, Toronto, ON M5T 2S8, Canada.
- Department of Surgery, University of Toronto, Toronto, ON M1C 1A4, Canada.
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M1C 1A4, Canada.
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27
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Liu LY, Zhang HJ, Luo LY, Pu JB, Liang WQ, Zhu CQ, Li YP, Wang PR, Zhang YY, Yang CY, Zhang ZJ. Blood and urinary metabolomic evidence validating traditional Chinese medicine diagnostic classification of major depressive disorder. Chin Med 2018; 13:53. [PMID: 30386416 PMCID: PMC6203264 DOI: 10.1186/s13020-018-0211-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 10/18/2018] [Indexed: 02/08/2023] Open
Abstract
Background Major depressive disorder (MDD) is a highly heterogeneous disease. Further classification may characterize its heterogeneity. The purpose of this study was to examine whether metabolomic variables could differentiate traditional Chinese medicine (TCM) diagnostic subtypes of MDD. Methods Fifty medication-free patients who were experiencing a recurrent depressive episode were classified into Liver Qi Stagnation (LQS, n = 30) and Heart and Spleen Deficiency (HSD, n = 20) subtypes according to TCM diagnosis. Healthy volunteers (n = 28) were included as controls. Gas chromatography-mass spectrometry (GC–MS) was used to examine serum and urinary metabolomic profiles. Results Twenty-eight metabolites were identified for good separations between TCM subtypes and healthy controls in serum samples. Both TCM subtypes had similar profiles in proteinogenic branched-chain amino acids (BCAAs) (valine, leucine, and isoleucine) and energy metabolism-related metabolites that were differentiated from healthy controls. The LQS subtype additionally differed from healthy controls in multiple amino acid metabolites that are involved in biosynthesis of monoamine and amino acid neurotransmitters, including phenylalanine, 3-hydroxybutric acid, o-tyrosine, glycine, l-tryptophan, and N-acetyl-l-aspartic acid. Threonic acid, methionine, stearic acid, and isobutyric acid are differentially associated with the two subtypes. Conclusions While both TCM subtypes are associated with aberrant BCAA and energy metabolism, the LQS subtype may represent an MDD subpopulation characterized by abnormalities in the biosynthesis of monoamine and amino acid neurotransmitters and closer associations with stress-related pathophysiology. The metabolites differentially associated with the two subtypes are promising biomarkers for predicting TCM subtype-specific antidepressant response [registered at http://www.clinicaltrials.gov (NCT02346682) on January 27, 2015]. Electronic supplementary material The online version of this article (10.1186/s13020-018-0211-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Lan-Ying Liu
- 1Department of Psychiatry, Tongde Hospital of Zhejiang Province, Hangzhou, 310012 Zhejiang China
| | - Hong-Jian Zhang
- 2Zhejiang Academy of Traditional Chinese Medicine, Hangzhou, 310007 Zhejiang China
| | - Li-Yuan Luo
- 1Department of Psychiatry, Tongde Hospital of Zhejiang Province, Hangzhou, 310012 Zhejiang China
| | - Jin-Bao Pu
- 2Zhejiang Academy of Traditional Chinese Medicine, Hangzhou, 310007 Zhejiang China
| | - Wei-Qing Liang
- 2Zhejiang Academy of Traditional Chinese Medicine, Hangzhou, 310007 Zhejiang China
| | - Chun-Qin Zhu
- 1Department of Psychiatry, Tongde Hospital of Zhejiang Province, Hangzhou, 310012 Zhejiang China
| | - Ya-Ping Li
- 3Department of Internal Chinese Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, 310012 Zhejiang China
| | - Pei-Rong Wang
- 1Department of Psychiatry, Tongde Hospital of Zhejiang Province, Hangzhou, 310012 Zhejiang China
| | - Yuan-Yuan Zhang
- 1Department of Psychiatry, Tongde Hospital of Zhejiang Province, Hangzhou, 310012 Zhejiang China
| | - Chun-Yu Yang
- 1Department of Psychiatry, Tongde Hospital of Zhejiang Province, Hangzhou, 310012 Zhejiang China
| | - Zhang-Jin Zhang
- 4School of Chinese Medicine, LKS Faculty of Medicine, The University of Hong Kong, 10 Sassoon Road, Pokfulam, Hong Kong, China
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Walther A, Cannistraci CV, Simons K, Durán C, Gerl MJ, Wehrli S, Kirschbaum C. Lipidomics in Major Depressive Disorder. Front Psychiatry 2018; 9:459. [PMID: 30374314 PMCID: PMC6196281 DOI: 10.3389/fpsyt.2018.00459] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 09/04/2018] [Indexed: 01/01/2023] Open
Abstract
Omic sciences coupled with novel computational approaches such as machine intelligence offer completely new approaches to major depressive disorder (MDD) research. The complexity of MDD's pathophysiology is being integrated into studies examining MDD's biology within the omic fields. Lipidomics, as a late-comer among other omic fields, is increasingly being recognized in psychiatric research because it has allowed the investigation of global lipid perturbations in patients suffering from MDD and indicated a crucial role of specific patterns of lipid alterations in the development and progression of MDD. Combinatorial lipid-markers with high classification power are being developed in order to assist MDD diagnosis, while rodent models of depression reveal lipidome changes and thereby unveil novel treatment targets for depression. In this systematic review, we provide an overview of current breakthroughs and future trends in the field of lipidomics in MDD research and thereby paving the way for precision medicine in MDD.
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Affiliation(s)
| | - Carlo Vittorio Cannistraci
- Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Department of Physics, TU Dresden, Dresden, Germany
- Brain Bio-Inspired Computing (BBC) Lab, IRCCS Centro Neurolesi “Bonino Pulejo”, Messina, Italy
| | | | - Claudio Durán
- Biomedical Cybernetics Group, Biotechnology Center (BIOTEC), Center for Molecular and Cellular Bioengineering (CMCB), Center for Systems Biology Dresden (CSBD), Department of Physics, TU Dresden, Dresden, Germany
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29
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Juruena MF, Bocharova M, Agustini B, Young AH. Atypical depression and non-atypical depression: Is HPA axis function a biomarker? A systematic review. J Affect Disord 2018; 233:45-67. [PMID: 29150144 DOI: 10.1016/j.jad.2017.09.052] [Citation(s) in RCA: 189] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2017] [Revised: 07/11/2017] [Accepted: 09/26/2017] [Indexed: 02/08/2023]
Abstract
BACKGROUND The link between the abnormalities of the Hypothalamic-pituitary-adrenal (HPA) axis and depression has been one of the most consistently reported findings in psychiatry. At the same time, multiple studies have demonstrated a stronger association between the increased activation of HPA-axis and melancholic, or endogenous depression subtype. This association has not been confirmed for the atypical subtype, and some researchers have suggested that as an antinomic depressive subtype, it may be associated with the opposite type, i.e. hypo-function, of the HPA-axis, similarly to PTSD. The purpose of this systematic review is to summarise existing studies addressing the abnormalities of the HPA-axis in melancholic and/or atypical depression. METHOD We conducted a systematic review in the literature by searching MEDLINE, PsycINFO, OvidSP and Embase databases until June 2017. The following search items were used: "hypothalamic-pituitary-adrenal" OR "HPA" OR "cortisol" OR "corticotropin releasing hormone" OR "corticotropin releasing factor" OR "glucocorticoid*" OR "adrenocorticotropic hormone" OR "ACTH" AND "atypical depression" OR "non-atypical depression" OR "melancholic depression" OR "non-melancholic depression" OR "endogenous depression" OR "endogenomorphic depression" OR "non-endogenous depression". Search limits were set to include papers in English or German language published in peer-reviewed journals at any period. All studies were scrutinized to determine the main methodological characteristics, and particularly possible sources of bias influencing the results reported. RESULTS We selected 48 relevant studies. Detailed analysis of the methodologies used in the studies revealed significant variability especially regarding the samples' definition comparing the HPA axis activity of melancholic patients to atypical depression, including healthy controls. The results were subdivided into 4 sections: (1) 27 studies which compared melancholic OR endogenous depression vs. non-melancholic or non-endogenous depression or controls; (2) 9 studies which compared atypical depression or atypical traits vs. non-atypical depression or controls; (3) 7 studies which compared melancholic or endogenous and atypical depression subtypes and (4) 5 studies which used a longitudinal design, comparing the measures of HPA-axis across two or more time points. While the majority of studies did confirm the association between melancholic depression and increased post-challenge cortisol levels, the association with increases in basal cortisol and basal ACTH were less consistent. Some studies, particularly those focusing on reversed vegetative symptoms, demonstrated a decrease in the activity of the HPA axis in atypical depression compared to controls, but the majority did not distinguish it from healthy controls. CONCLUSIONS In conclusion, our findings indicate that there is a difference in the activity of the HPA-axis between melancholic and atypical depressive subtypes. However, these are more likely explained by hypercortisolism in melancholia; and most often normal than decreased function in atypical depression. Further research should seek to distinguish a particular subtype of depression linked to HPA-axis abnormalities, based on symptom profile, with a focus on vegetative symptoms, neuroendocrine probes, and the history of adverse childhood events. New insights into the dichotomy addressed in this review might be obtained from genetic and epigenetic studies of HPA-axis related genes in both subtypes, with an emphasis on the presence of vegetative symptoms.
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Affiliation(s)
- Mario F Juruena
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) and King's College London, UK; Department of Neuroscience and Behavior, School of Medicine of Ribeirao Preto, University of Sao Paulo, Sao Paulo, Brazil.
| | - Mariia Bocharova
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) and King's College London, UK
| | - Bruno Agustini
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) and King's College London, UK
| | - Allan H Young
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) and King's College London, UK
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30
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Musil R, Seemüller F, Meyer S, Spellmann I, Adli M, Bauer M, Kronmüller KT, Brieger P, Laux G, Bender W, Heuser I, Fisher R, Gaebel W, Schennach R, Möller HJ, Riedel M. Subtypes of depression and their overlap in a naturalistic inpatient sample of major depressive disorder. Int J Methods Psychiatr Res 2018; 27:e1569. [PMID: 29498147 PMCID: PMC6877097 DOI: 10.1002/mpr.1569] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2016] [Revised: 03/31/2017] [Accepted: 04/10/2017] [Indexed: 12/18/2022] Open
Abstract
Subtyping depression is important in order to further delineate biological causes of depressive syndromes. The aim of this study was to evaluate clinical and outcome characteristics of distinct subtypes of depression and to assess proportion and features of patients fulfilling criteria for more than one subtype. Melancholic, atypical and anxious subtypes of depression were assessed in a naturalistic sample of 833 inpatients using DSM-IV specifiers based on operationalized criteria. Baseline characteristics and outcome criteria at discharge were compared between distinct subtypes and their overlap. A substantial proportion of patients (16%) were classified with more than one subtype of depression, 28% were of the distinct anxious, 7% of the distinct atypical and 5% of the distinct melancholic subtype. Distinct melancholic patients had shortest duration of episode, highest baseline depression severity, but were more often early improvers; distinct anxious patients had higher NEO-Five Factor Inventory (NEO-FFI) neuroticism scores compared with patients with unspecific subtype. Melancholic patients with overlap of anxious features had worse treatment outcome compared to distinct melancholic and distinct anxious subtype. Distinct subtypes differed in only few variables and patients with overlap of depression subtypes may have independent clinical and outcome characteristics. Studies investigating biological causes of subtypes of depression should take influence of features of other subtypes into account.
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Affiliation(s)
- Richard Musil
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Florian Seemüller
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany.,Department of Psychiatry and Psychotherapy, kbo-Lech-Mangfall-Klinik, Garmisch-Partenkirchen, Garmisch-Partenkirchen, Germany
| | - Sebastian Meyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany.,Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zürich, Switzerland.,Institute of Medical Informatics, Biometry, and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Ilja Spellmann
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany.,Bezirkskrankenhaus Kaufbeuren, Bezirkskliniken Schwaben, Kaufbeuren, Germany
| | - Mazda Adli
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte (CCM), Berlin, Germany
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Klaus-Thomas Kronmüller
- Department of Psychiatry and Psychotherapy, University of Heidelberg, Heidelberg, Germany.,LWL-Klinikum, Gütersloh, Germany
| | - Peter Brieger
- Department of Psychiatry and Psychotherapy, Martin-Luther University Halle-Wittenberg, Halle, Germany.,Department of Psychiatry and Psychotherapy, kbo-Isar-Amper-Klinikum Munich East, Haar, Gemany
| | - Gerd Laux
- kbo-Inn-Salzach-Klinikum, Department of Psychiatry and Psychotherapy, Wasserburg, Gemany
| | - Wolfram Bender
- Department of Psychiatry and Psychotherapy, kbo-Isar-Amper-Klinikum Munich East, Haar, Gemany
| | - Isabella Heuser
- Department of Psychiatry, Charité - Campus Benjamin Franklin (CBF), Berlin, Germany
| | - Robert Fisher
- Department of Psychiatry and Psychotherapy, Auguste-Viktoria-Krankenhaus, Berlin, Germany.,South Hackney CMHT, Donald WinniCott Centre, London, UK
| | - Wolfgang Gaebel
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany
| | - Rebecca Schennach
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany.,Schön Klinik Roseneck, Prien, Rosenheim, Prien am Chiemsee, Germany
| | - Hans-Jürgen Möller
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Michael Riedel
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany.,Klinik für Psychiatrie & Psychotherapie II, Zentrum für Psychiatrie Calw Klinikum Nordschwarzwald, Calw-Hirsau, Germany
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Replicable and Coupled Changes in Innate and Adaptive Immune Gene Expression in Two Case-Control Studies of Blood Microarrays in Major Depressive Disorder. Biol Psychiatry 2018; 83:70-80. [PMID: 28688579 PMCID: PMC5720346 DOI: 10.1016/j.biopsych.2017.01.021] [Citation(s) in RCA: 140] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 01/08/2017] [Accepted: 01/12/2017] [Indexed: 12/22/2022]
Abstract
BACKGROUND Peripheral inflammation is often associated with major depressive disorder (MDD), and immunological biomarkers of depression remain a focus of investigation. METHODS We used microarray data on whole blood from two independent case-control studies of MDD: the GlaxoSmithKline-High-Throughput Disease-specific target Identification Program [GSK-HiTDiP] study (113 patients and 57 healthy control subjects) and the Janssen-Brain Resource Company study (94 patients and 100 control subjects). Genome-wide differential gene expression analysis (18,863 probes) resulted in a p value for each gene in each study. A Bayesian method identified the largest p-value threshold (q = .025) associated with twice the number of genes differentially expressed in both studies compared with the number of coincidental case-control differences expected by chance. RESULTS A total of 165 genes were differentially expressed in both studies with concordant direction of fold change. The 90 genes overexpressed (or UP genes) in MDD were significantly enriched for immune response to infection, were concentrated in a module of the gene coexpression network associated with innate immunity, and included clusters of genes with correlated expression in monocytes, monocyte-derived dendritic cells, and neutrophils. In contrast, the 75 genes underexpressed (or DOWN genes) in MDD were associated with the adaptive immune response and included clusters of genes with correlated expression in T cells, natural killer cells, and erythroblasts. Consistently, the MDD patients with overexpression of UP genes also had underexpression of DOWN genes (correlation > .70 in both studies). CONCLUSIONS MDD was replicably associated with proinflammatory activation of the peripheral innate immune system, coupled with relative inactivation of the adaptive immune system, indicating the potential of transcriptional biomarkers for immunological stratification of patients with depression.
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32
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A healthy mix of emotions: underlying biological pathways linking emotions to physical health. Curr Opin Behav Sci 2017. [DOI: 10.1016/j.cobeha.2017.05.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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Plasma Metabolites Predict Severity of Depression and Suicidal Ideation in Psychiatric Patients-A Multicenter Pilot Analysis. PLoS One 2016; 11:e0165267. [PMID: 27984586 PMCID: PMC5161310 DOI: 10.1371/journal.pone.0165267] [Citation(s) in RCA: 85] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 10/07/2016] [Indexed: 12/27/2022] Open
Abstract
Evaluating the severity of depression (SOD), especially suicidal ideation (SI), is crucial in the treatment of not only patients with mood disorders but also psychiatric patients in general. SOD has been assessed on interviews such as the Hamilton Rating Scale for Depression (HAMD)-17, and/or self-administered questionnaires such as the Patient Health Questionnaire (PHQ)-9. However, these evaluation systems have relied on a person's subjective information, which sometimes lead to difficulties in clinical settings. To resolve this limitation, a more objective SOD evaluation system is needed. Herein, we collected clinical data including HAMD-17/PHQ-9 and blood plasma of psychiatric patients from three independent clinical centers. We performed metabolome analysis of blood plasma using liquid chromatography mass spectrometry (LC-MS), and 123 metabolites were detected. Interestingly, five plasma metabolites (3-hydroxybutyrate (3HB), betaine, citrate, creatinine, and gamma-aminobutyric acid (GABA)) are commonly associated with SOD in all three independent cohort sets regardless of the presence or absence of medication and diagnostic difference. In addition, we have shown several metabolites are independently associated with sub-symptoms of depression including SI. We successfully created a classification model to discriminate depressive patients with or without SI by machine learning technique. Finally, we produced a pilot algorithm to predict a grade of SI with citrate and kynurenine. The above metabolites may have strongly been associated with the underlying novel biological pathophysiology of SOD. We should explore the biological impact of these metabolites on depressive symptoms by utilizing a cross species study model with human and rodents. The present multicenter pilot study offers a potential utility for measuring blood metabolites as a novel objective tool for not only assessing SOD but also evaluating therapeutic efficacy in clinical practice. In addition, modification of these metabolites by diet and/or medications may be a novel therapeutic target for depression. To clarify these aspects, clinical trials measuring metabolites before/after interventions should be conducted. Larger cohort studies including non-clinical subjects are also warranted to clarify our pilot findings.
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