1
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Verhoeven JE, Wolkowitz OM, Barr Satz I, Conklin Q, Lamers F, Lavebratt C, Lin J, Lindqvist D, Mayer SE, Melas PA, Milaneschi Y, Picard M, Rampersaud R, Rasgon N, Ridout K, Söderberg Veibäck G, Trumpff C, Tyrka AR, Watson K, Wu GWY, Yang R, Zannas AS, Han LKM, Månsson KNT. The researcher's guide to selecting biomarkers in mental health studies. Bioessays 2024; 46:e2300246. [PMID: 39258367 DOI: 10.1002/bies.202300246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 07/01/2024] [Accepted: 07/03/2024] [Indexed: 09/12/2024]
Abstract
Clinical mental health researchers may understandably struggle with how to incorporate biological assessments in clinical research. The options are numerous and are described in a vast and complex body of literature. Here we provide guidelines to assist mental health researchers seeking to include biological measures in their studies. Apart from a focus on behavioral outcomes as measured via interviews or questionnaires, we advocate for a focus on biological pathways in clinical trials and epidemiological studies that may help clarify pathophysiology and mechanisms of action, delineate biological subgroups of participants, mediate treatment effects, and inform personalized treatment strategies. With this paper we aim to bridge the gap between clinical and biological mental health research by (1) discussing the clinical relevance, measurement reliability, and feasibility of relevant peripheral biomarkers; (2) addressing five types of biological tissues, namely blood, saliva, urine, stool and hair; and (3) providing information on how to control sources of measurement variability.
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Affiliation(s)
- Josine E Verhoeven
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Mental Health program, Amsterdam, The Netherlands
| | - Owen M Wolkowitz
- Department of Psychiatry and Behavioral Sciences, and Weill Institute for Neurosciences, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Isaac Barr Satz
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Quinn Conklin
- Center for Mind and Brain, University of California, Davis, California, USA
- Center for Health and Community, University of California, San Francisco, California, USA
| | - Femke Lamers
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Mental Health program, Amsterdam, The Netherlands
| | - Catharina Lavebratt
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, L8:00, Karolinska University Hospital, Stockholm, Sweden
| | - Jue Lin
- Department of Biochemistry and Biophysics, University of California, San Francisco, California, USA
| | - Daniel Lindqvist
- Unit for Biological and Precision Psychiatry, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Office for Psychiatry and Habilitation, Psychiatry Research Skåne, Region Skåne, Lund, Sweden
| | - Stefanie E Mayer
- Department of Psychiatry and Behavioral Sciences, and Weill Institute for Neurosciences, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Philippe A Melas
- Center for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Mental Health program, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Complex Trait Genetics, Amsterdam, The Netherlands
| | - Martin Picard
- Department of Psychiatry, Division of Behavioral Medicine, College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA
- Department of Neurology, H. Houston Merritt Center, Columbia Translational Neuroscience Initiative, College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA
- New York State Psychiatric Institute, New York, USA
- Robert N Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, New York, USA
| | - Ryan Rampersaud
- Department of Psychiatry and Behavioral Sciences, and Weill Institute for Neurosciences, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Natalie Rasgon
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Kathryn Ridout
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
- Department of Psychiatry, Kaiser Permanente, Santa Rosa Medical Center, Santa Rosa, California, USA
| | - Gustav Söderberg Veibäck
- Unit for Biological and Precision Psychiatry, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Office for Psychiatry and Habilitation, Psychiatry Research Skåne, Region Skåne, Lund, Sweden
| | - Caroline Trumpff
- Department of Psychiatry, Division of Behavioral Medicine, College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA
| | - Audrey R Tyrka
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Kathleen Watson
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Gwyneth Winnie Y Wu
- Department of Psychiatry and Behavioral Sciences, and Weill Institute for Neurosciences, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Ruoting Yang
- Medical Readiness Systems Biology, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA
| | - Anthony S Zannas
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Laura K M Han
- Department of Psychiatry, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia
- Orygen, Parkville, Victoria, Australia
| | - Kristoffer N T Månsson
- Center for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Psychology and Psychotherapy, Babeș-Bolyai University, Cluj-Napoca, Romania
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Park Y, Park S, Lee M. Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review. J Affect Disord 2024; 361:445-456. [PMID: 38889858 DOI: 10.1016/j.jad.2024.06.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 11/27/2023] [Accepted: 06/14/2024] [Indexed: 06/20/2024]
Abstract
OBJECTIVES This study underscores the importance of exploring AI's creative applications in treating depressive disorders to revolutionize mental health care. Through innovative integration of AI technologies, the research confirms their positive effects on preventing, diagnosing, and treating depression. The systematic review establishes an evidence base for AI in depression management, offering directions for effective interventions. METHODS This systematic literature review investigates the effectiveness of AI in depression management by analyzing studies from January 1, 2017, to May 31, 2022. Utilizing search engines like IEEE Xplore, PubMed, and Web of Science, the review focused on keywords such as Depression/Mental Health, Machine Learning/Artificial Intelligence, and Prediction/Diagnosis. The analysis of 95 documents involved classification based on use, data type, and algorithm type. RESULTS The study revealed that AI in depression management excelled in accuracy, particularly in monitoring and prediction. Biomarker-derived data demonstrated the highest accuracy, with the CNN algorithm proving most effective. The findings affirm the therapeutic benefits of AI, including treatment, detection, and disease prediction, highlighting its potential in analyzing monitored data for depression management. LIMITATIONS This study exclusively examined the application of AI in individuals with depressive disorders. Interpretation should be cautious due to the limited scope of subjects to this specific population. CONCLUSIONS To introduce digital healthcare and therapies for ongoing depression management, it's crucial to present empirical evidence on the medical fee payment system, safety, and efficacy. These findings support enhanced medical accessibility through digital healthcare, offering personalized disease management for patients seeking non-face-to-face treatment.
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Affiliation(s)
- Yoonseo Park
- Department of Convergence Healthcare Medicine, Ajou University, Suwon, South Korea
| | - Sewon Park
- Department of Medical Science, Ajou University School of Medicine, Suwon, South Korea
| | - Munjae Lee
- Department of Medical Science, Ajou University School of Medicine, Suwon, South Korea.
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Taylor B, Hobensack M, Niño de Rivera S, Zhao Y, Masterson Creber R, Cato K. Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review. JMIR Nurs 2024; 7:e54810. [PMID: 39028994 PMCID: PMC11297379 DOI: 10.2196/54810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 04/16/2024] [Accepted: 04/22/2024] [Indexed: 07/21/2024] Open
Abstract
BACKGROUND Depression is one of the most common mental disorders that affects >300 million people worldwide. There is a shortage of providers trained in the provision of mental health care, and the nursing workforce is essential in filling this gap. The diagnosis of depression relies heavily on self-reported symptoms and clinical interviews, which are subject to implicit biases. The omics methods, including genomics, transcriptomics, epigenomics, and microbiomics, are novel methods for identifying the biological underpinnings of depression. Machine learning is used to analyze genomic data that includes large, heterogeneous, and multidimensional data sets. OBJECTIVE This scoping review aims to review the existing literature on machine learning methods for omics data analysis to identify individuals with depression, with the goal of providing insight into alternative objective and driven insights into the diagnostic process for depression. METHODS This scoping review was reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Searches were conducted in 3 databases to identify relevant publications. A total of 3 independent researchers performed screening, and discrepancies were resolved by consensus. Critical appraisal was performed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies. RESULTS The screening process identified 15 relevant papers. The omics methods included genomics, transcriptomics, epigenomics, multiomics, and microbiomics, and machine learning methods included random forest, support vector machine, k-nearest neighbor, and artificial neural network. CONCLUSIONS The findings of this scoping review indicate that the omics methods had similar performance in identifying omics variants associated with depression. All machine learning methods performed well based on their performance metrics. When variants in omics data are associated with an increased risk of depression, the important next step is for clinicians, especially nurses, to assess individuals for symptoms of depression and provide a diagnosis and any necessary treatment.
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Affiliation(s)
- Brittany Taylor
- School of Nursing, Columbia University, New York, NY, United States
| | - Mollie Hobensack
- Brookdale Department of Geriatrics and Palliative Care, Icahn School of Medicine, Mount Sinai Health System, New York, NY, United States
| | | | - Yihong Zhao
- School of Nursing, Columbia University, New York, NY, United States
| | | | - Kenrick Cato
- School of Nursing, University of Pennsylvania, Philadelphia, PA, United States
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Sokolov AV, Schiöth HB. Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches. Transl Psychiatry 2024; 14:287. [PMID: 39009577 PMCID: PMC11250806 DOI: 10.1038/s41398-024-02992-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 06/24/2024] [Accepted: 06/27/2024] [Indexed: 07/17/2024] Open
Abstract
The causes of depression are complex, and the current diagnosis methods rely solely on psychiatric evaluations with no incorporation of laboratory biomarkers in clinical practices. We investigated the stability of blood DNA methylation depression signatures in six different populations using six public and two domestic cohorts (n = 1942) conducting mega-analysis and meta-analysis of the individual studies. We evaluated 12 machine learning and deep learning strategies for depression classification both in cross-validation (CV) and in hold-out tests using merged data from 8 separate batches, constructing models with both biased and unbiased feature selection. We found 1987 CpG sites related to depression in both mega- and meta-analysis at the nominal level, and the associated genes were nominally related to axon guidance and immune pathways based on enrichment analysis and eQTM data. Random forest classifiers achieved the highest performance (AUC 0.73 and 0.76) in CV and hold-out tests respectively on the batch-level processed data. In contrast, the methylation showed low predictive power (all AUCs < 0.57) for all classifiers in CV and no predictive power in hold-out tests when used with harmonized data. All models achieved significantly better performance (>14% gain in AUCs) with pre-selected features (selection bias), with some of the models (joint autoencoder-classifier) reaching AUCs of up to 0.91 in the final testing regardless of data preparation. Different algorithmic feature selection approaches may outperform limma, however, random forest models perform well regardless of the strategy. The results provide an overview over potential future biomarkers for depression and highlight many important methodological aspects for DNA methylation-based depression profiling including the use of machine learning strategies.
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Affiliation(s)
- Aleksandr V Sokolov
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden
| | - Helgi B Schiöth
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, Uppsala University, Uppsala, Sweden.
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Ko YK, Chi S, Nam GH, Baek KW, Ahn K, Ahn Y, Kang J, Lee MS, Gim JA. Epigenome-wide Association Study for Tic Disorders in Children: A Preliminary Study in Korean Population. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2024; 22:295-305. [PMID: 38627076 PMCID: PMC11024688 DOI: 10.9758/cpn.23.1099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 09/24/2023] [Accepted: 11/24/2023] [Indexed: 04/20/2024]
Abstract
Objective : Tic disorders can affect the quality of life in both childhood and adolescence. Many factors are involved in the etiology of tic disorders, and the genetic and epigenetic factors of tic disorders are considered complex and heterogeneous. Methods : In this study, the differentially methylated regions (DMRs) between normal controls (n = 24; aged 6-15; 7 females) and patients with tic disorders (n = 16; aged 6-15; 5 females) were analyzed. We performed an epigenome-wide association study of tic disorders in Korean children. The tics were assessed using Yale Global Tic Severity Scale. The DNA methylation data consisted of 726,945 cytosine phosphate guanine (CpG) sites, assessed using the Illumina Infinium MethylationEPIC (850k) BeadChip. The DNA methylation data of the 40 participants were retrieved, and DMRs between the four groups based on sex and tic disorder were identified. From 28 male and 16 female samples, 37 and 38 DMRs were identified, respectively. We analyzed the enriched terms and visualized the network, heatmap, and upset plot. Results : In male, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed hypomethylated patterns in the ligand, receptor, and second signal transductors of the PI3K-Akt and MAPK signaling pathway (most cells were indicated as green color), and in female, the opposite patterns were revealed (most cells were indicated as red color). Five mental disorder-related enriched terms were identified in the network analysis. Conclusion : Here, we provide insights into the epigenetic mechanisms of tic disorders. Abnormal DNA methylation patterns are associated with mental disorder-related symptoms.
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Affiliation(s)
- Young Kyung Ko
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University Guro Hospital, Seoul, Korea
| | - Suhyuk Chi
- Department of Psychiatry, Korea University Guro Hospital, Seoul, Korea
| | - Gyu-Hwi Nam
- PhileKorea Technology Co. Ltd., Daejeon, Korea
| | - Kyung-Wan Baek
- Department of Physical Education, Gyeongsang National University, Jinju, Korea
| | | | | | - June Kang
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Moon-Soo Lee
- Department of Psychiatry, Korea University Guro Hospital, Seoul, Korea
| | - Jeong-An Gim
- Department of Medical Science, Soonchunhyang University, Asan, Korea
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Ehtemam H, Sadeghi Esfahlani S, Sanaei A, Ghaemi MM, Hajesmaeel-Gohari S, Rahimisadegh R, Bahaadinbeigy K, Ghasemian F, Shirvani H. Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies. BMC Med Inform Decis Mak 2024; 24:138. [PMID: 38802823 PMCID: PMC11129374 DOI: 10.1186/s12911-024-02524-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 04/30/2024] [Indexed: 05/29/2024] Open
Abstract
OBJECTIVE Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction of suicide attempts. METHOD A systematic review was performed using PubMed, Scopus, Web of Science and SID databases. We aim to evaluate the performance of ML algorithms and summarize their effects, gather relevant and reliable information to synthesize existing evidence, identify knowledge gaps, and provide a comprehensive list of the suicide risk factors using mixed method approach. RESULTS Forty-one studies published between 2011 and 2022, which matched inclusion criteria, were chosen as suitable. We included studies aimed at predicting the suicide risk by machine learning algorithms except natural language processing (NLP) and image processing. The neural network (NN) algorithm exhibited the lowest accuracy at 0.70, whereas the random forest demonstrated the highest accuracy, reaching 0.94. The study assessed the COX and random forest models and observed a minimum area under the curve (AUC) value of 0.54. In contrast, the XGBoost classifier yielded the highest AUC value, reaching 0.97. These specific AUC values emphasize the algorithm-specific performance in capturing the trade-off between sensitivity and specificity for suicide risk prediction. Furthermore, our investigation identified several common suicide risk factors, including age, gender, substance abuse, depression, anxiety, alcohol consumption, marital status, income, education, and occupation. This comprehensive analysis contributes valuable insights into the multifaceted nature of suicide risk, providing a foundation for targeted preventive strategies and intervention efforts. CONCLUSIONS The effectiveness of ML algorithms and their application in predicting suicide risk has been controversial. There is a need for more studies on these algorithms in clinical settings, and the related ethical concerns require further clarification.
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Affiliation(s)
- Houriyeh Ehtemam
- School of Engineering and the Built Environment, Anglia Ruskin University, Chelmsford, UK
| | | | - Alireza Sanaei
- School of Engineering and the Built Environment, Anglia Ruskin University, Chelmsford, UK
| | - Mohammad Mehdi Ghaemi
- Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
| | - Sadrieh Hajesmaeel-Gohari
- Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Rohaneh Rahimisadegh
- Health Services Management Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Kambiz Bahaadinbeigy
- Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| | - Fahimeh Ghasemian
- Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Hassan Shirvani
- School of Engineering and the Built Environment, Anglia Ruskin University, Chelmsford, UK
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Karetnikov DI, Romanov SE, Baklaushev VP, Laktionov PP. Age Prediction Using DNA Methylation Heterogeneity Metrics. Int J Mol Sci 2024; 25:4967. [PMID: 38732187 PMCID: PMC11084170 DOI: 10.3390/ijms25094967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 04/27/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024] Open
Abstract
Dynamic changes in genomic DNA methylation patterns govern the epigenetic developmental programs and accompany the organism's aging. Epigenetic clock (eAge) algorithms utilize DNA methylation to estimate the age and risk factors for diseases as well as analyze the impact of various interventions. High-throughput bisulfite sequencing methods, such as reduced-representation bisulfite sequencing (RRBS) or whole genome bisulfite sequencing (WGBS), provide an opportunity to identify the genomic regions of disordered or heterogeneous DNA methylation, which might be associated with cell-type heterogeneity, DNA methylation erosion, and allele-specific methylation. We systematically evaluated the applicability of five scores assessing the variability of methylation patterns by evaluating within-sample heterogeneity (WSH) to construct human blood epigenetic clock models using RRBS data. The best performance was demonstrated by the model based on a metric designed to assess DNA methylation erosion with an MAE of 3.686 years. We also trained a prediction model that uses the average methylation level over genomic regions. Although this region-based model was relatively more efficient than the WSH-based model, the latter required the analysis of just a few short genomic regions and, therefore, could be a useful tool to design a reduced epigenetic clock that is analyzed by targeted next-generation sequencing.
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Affiliation(s)
- Dmitry I. Karetnikov
- Federal Research Center Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Stanislav E. Romanov
- Epigenetics Laboratory, Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Institute of Molecular and Cellular Biology, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Vladimir P. Baklaushev
- Federal Center for Brain and Neurotechnologies, Federal Medical and Biological Agency of Russia, 117513 Moscow, Russia
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
- Department of Medical Nanobiotechnology, Medical and Biological Faculty, Pirogov Russian National Research Medical University, Ministry of Health of the Russian Federation, 117997 Moscow, Russia
| | - Petr P. Laktionov
- Epigenetics Laboratory, Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Institute of Molecular and Cellular Biology, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
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Pigoni A, Delvecchio G, Turtulici N, Madonna D, Pietrini P, Cecchetti L, Brambilla P. Machine learning and the prediction of suicide in psychiatric populations: a systematic review. Transl Psychiatry 2024; 14:140. [PMID: 38461283 PMCID: PMC10925059 DOI: 10.1038/s41398-024-02852-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 02/22/2024] [Accepted: 02/22/2024] [Indexed: 03/11/2024] Open
Abstract
Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric populations, a formal psychiatric diagnosis emerged as a strong predictor of suicidal risk, overshadowing more subtle risk factors specific to distinct populations. To overcome this limitation, we conducted a systematic review of ML studies evaluating suicidal behaviors exclusively in psychiatric clinical populations. A systematic literature search was performed from inception through November 17, 2022 on PubMed, EMBASE, and Scopus following the PRISMA guidelines. Original research using ML techniques to assess the risk of suicide or predict suicide attempts in the psychiatric population were included. An assessment for bias risk was performed using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. About 1032 studies were retrieved, and 81 satisfied the inclusion criteria and were included for qualitative synthesis. Clinical and demographic features were the most frequently employed and random forest, support vector machine, and convolutional neural network performed better in terms of accuracy than other algorithms when directly compared. Despite heterogeneity in procedures, most studies reported an accuracy of 70% or greater based on features such as previous attempts, severity of the disorder, and pharmacological treatments. Although the evidence reported is promising, ML algorithms for suicidal prediction still present limitations, including the lack of neurobiological and imaging data and the lack of external validation samples. Overcoming these issues may lead to the development of models to adopt in clinical practice. Further research is warranted to boost a field that holds the potential to critically impact suicide mortality.
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Affiliation(s)
- Alessandro Pigoni
- Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Nunzio Turtulici
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Domenico Madonna
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Pietro Pietrini
- MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Luca Cecchetti
- Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
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9
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Pereira CA, Reis-de-Oliveira G, Pierone BC, Martins-de-Souza D, Kaster MP. Depicting the molecular features of suicidal behavior: a review from an "omics" perspective. Psychiatry Res 2024; 332:115682. [PMID: 38198856 DOI: 10.1016/j.psychres.2023.115682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 12/05/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024]
Abstract
Background Suicide is one of the leading global causes of death. Behavior patterns from suicide ideation to completion are complex, involving multiple risk factors. Advances in technologies and large-scale bioinformatic tools are changing how we approach biomedical problems. The "omics" field may provide new knowledge about suicidal behavior to improve identification of relevant biological pathways associated with suicidal behavior. Methods We reviewed transcriptomic, proteomic, and metabolomic studies conducted in blood and post-mortem brains from individuals who experienced suicide or suicidal behavior. Omics data were combined using systems biology in silico, aiming at identifying major biological mechanisms and key molecules associated with suicide. Results Post-mortem samples of suicide completers indicate major dysregulations in pathways associated with glial cells (astrocytes and microglia), neurotransmission (GABAergic and glutamatergic systems), neuroplasticity and cell survivor, immune responses and energy homeostasis. In the periphery, studies found alterations in molecules involved in immune responses, polyamines, lipid transport, energy homeostasis, and amino and nucleic acid metabolism. Limitations We included only exploratory, non-hypothesis-driven studies; most studies only included one brain region and whole tissue analysis, and focused on suicide completers who were white males with almost none confounding factors. Conclusions We can highlight the importance of synaptic function, especially the balance between the inhibitory and excitatory synapses, and mechanisms associated with neuroplasticity, common pathways associated with psychiatric disorders. However, some of the pathways highlighted in this review, such as transcriptional factors associated with RNA splicing, formation of cortical connections, and gliogenesis, point to mechanisms that still need to be explored.
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Affiliation(s)
- Caibe Alves Pereira
- Laboratory of Translational Neurosciences, Department of Biochemistry, Federal University of Santa Catarina (UFSC), Florianopolis, Santa Catarina, Brazil
| | - Guilherme Reis-de-Oliveira
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Bruna Caroline Pierone
- Laboratory of Translational Neurosciences, Department of Biochemistry, Federal University of Santa Catarina (UFSC), Florianopolis, Santa Catarina, Brazil
| | - Daniel Martins-de-Souza
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, SP, Brazil; Instituto Nacional de Biomarcadores Em Neuropsiquiatria (INBION) Conselho Nacional de Desenvolvimento Científico E Tecnológico, São Paulo, Brazil; Experimental Medicine Research Cluster (EMRC), University of Campinas, Campinas, SP, Brazil; D'Or Institute for Research and Education (IDOR), São Paulo, Brazil; INCT in Modelling Human Complex Diseases with 3D Platforms (Model3D), São Paulo, Brazil.
| | - Manuella Pinto Kaster
- Laboratory of Translational Neurosciences, Department of Biochemistry, Federal University of Santa Catarina (UFSC), Florianopolis, Santa Catarina, Brazil.
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Tong L, Shi W, Isgut M, Zhong Y, Lais P, Gloster L, Sun J, Swain A, Giuste F, Wang MD. Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence. IEEE Rev Biomed Eng 2024; 17:80-97. [PMID: 37824325 DOI: 10.1109/rbme.2023.3324264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.
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11
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Habets PC, Thomas RM, Milaneschi Y, Jansen R, Pool R, Peyrot WJ, Penninx BWJH, Meijer OC, van Wingen GA, Vinkers CH. Multimodal Data Integration Advances Longitudinal Prediction of the Naturalistic Course of Depression and Reveals a Multimodal Signature of Remission During 2-Year Follow-up. Biol Psychiatry 2023; 94:948-958. [PMID: 37330166 DOI: 10.1016/j.biopsych.2023.05.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 05/11/2023] [Accepted: 05/30/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND The ability to predict the disease course of individuals with major depressive disorder (MDD) is essential for optimal treatment planning. Here, we used a data-driven machine learning approach to assess the predictive value of different sets of biological data (whole-blood proteomics, lipid metabolomics, transcriptomics, genetics), both separately and added to clinical baseline variables, for the longitudinal prediction of 2-year remission status in MDD at the individual-subject level. METHODS Prediction models were trained and cross-validated in a sample of 643 patients with current MDD (2-year remission n = 325) and subsequently tested for performance in 161 individuals with MDD (2-year remission n = 82). RESULTS Proteomics data showed the best unimodal data predictions (area under the receiver operating characteristic curve = 0.68). Adding proteomic to clinical data at baseline significantly improved 2-year MDD remission predictions (area under the receiver operating characteristic curve = 0.63 vs. 0.78, p = .013), while the addition of other omics data to clinical data did not yield significantly improved model performance. Feature importance and enrichment analysis revealed that proteomic analytes were involved in inflammatory response and lipid metabolism, with fibrinogen levels showing the highest variable importance, followed by symptom severity. Machine learning models outperformed psychiatrists' ability to predict 2-year remission status (balanced accuracy = 71% vs. 55%). CONCLUSIONS This study showed the added predictive value of combining proteomic data, but not other omics data, with clinical data for the prediction of 2-year remission status in MDD. Our results reveal a novel multimodal signature of 2-year MDD remission status that shows clinical potential for individual MDD disease course predictions from baseline measurements.
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Affiliation(s)
- Philippe C Habets
- Department of Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands; Department of Internal Medicine, section Endocrinology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Rajat M Thomas
- Department of Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Yuri Milaneschi
- Department of Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Rick Jansen
- Department of Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Rene Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands
| | - Wouter J Peyrot
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands; Department of Complex Traits Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit, Amsterdam, the Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Onno C Meijer
- Department of Internal Medicine, section Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
| | - Guido A van Wingen
- Department of Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
| | - Christiaan H Vinkers
- Department of Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, Vrije Universiteit, Amsterdam, the Netherlands
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Kamli H, Shaikh A, Bappi MH, Raposo A, Ahmad MF, Sonia FA, Akbor MS, Prottay AAS, Gonçalves SA, Araújo IM, Coutinho HDM, Elbendary EY, Lho LH, Han H, Islam MT. Sclareol exerts synergistic antidepressant effects with quercetin and caffeine, possibly suppressing GABAergic transmission in chicks. Biomed Pharmacother 2023; 168:115768. [PMID: 37866001 DOI: 10.1016/j.biopha.2023.115768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/17/2023] [Accepted: 10/17/2023] [Indexed: 10/24/2023] Open
Abstract
This study evaluated the effects of sclareol (SCL) with or without caffeine (CAF) and quercetin (QUR) using in-vivo and in-silico studies. For this, 5-day-old chicks weighing between 45 and 48 g were randomly divided into five groups and treated accordingly. The chicks were monitored to compare the occurrence, latency, and duration of sleep as well as the loss and gain of righting reflex in response to SCL-10 mg/kg, CAF-10 mg/kg, and QUR-50 mg/kg using a thiopental sodium (TS)-induced sleeping model. Data were analyzed by one-way ANOVA followed by t-Student-Newman-Keuls' as a posthoc test at 95% confidence intervals with multiple comparisons. An in-silico study was also performed to investigate the possible antidepressant mechanisms of the test and/or standard drugs with different subunits of GABAA receptors. In comparison to the SCL, CAF, and QUR individual groups, SCL+CAF+QUR significantly increased the latency while decreasing the length of sleep. The incidence of loss and gain of the righting reflex was also modulated in the combination group. SCL showed better interaction with GABAA (α2 and α5) subunits than QUR with α2, α3, and α5. All these compounds showed stronger interactions with the GABAA receptor subunits than the standard CAF. Taken together, SCL, CAF, and QUR reduced the TS-induced righting reflex and sleeping time in the combination group more than in the individual treatments. SCL may show its antidepressant effects, possibly through interactions with GABAA receptor subunits.
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Affiliation(s)
- Hossam Kamli
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia
| | - Ahmad Shaikh
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia
| | - Mehedi Hasan Bappi
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
| | - António Raposo
- CBIOS (Research Center for Biosciences and Health Technologies), Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, Portugal
| | - Md Faruque Ahmad
- Department of Clinical Nutrition, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia
| | - Fatema Akter Sonia
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
| | - Md Showkoth Akbor
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
| | - Abdullah Al Shamsh Prottay
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
| | - Sheila Alves Gonçalves
- Department of Biological Chemistry, Laboratory of Microbiology and Molecular Biology, Program of Post-Graduation in Molecular Bioprospection, Regional University of Cariri, Crato, CE 63105-000, Brazil
| | - Isaac Moura Araújo
- Department of Biological Chemistry, Laboratory of Microbiology and Molecular Biology, Program of Post-Graduation in Molecular Bioprospection, Regional University of Cariri, Crato, CE 63105-000, Brazil
| | - Henrique Douglas Melo Coutinho
- Department of Biological Chemistry, Laboratory of Microbiology and Molecular Biology, Program of Post-Graduation in Molecular Bioprospection, Regional University of Cariri, Crato, CE 63105-000, Brazil
| | - Ehab Y Elbendary
- Department of Clinical Nutrition, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia
| | - Linda Heejung Lho
- College of Business Division of Tourism and Hotel Management, Cheongju University, 298 Daesung-ro, Cheongwon-gu, Cheongju-si, Chungcheongbuk-do 28503, Republic of Korea.
| | - Heesup Han
- College of Hospitality and Tourism Management, Sejong University, 98 Gunja-Dong, Gwanjin-Gu, Seoul 143-747, Republic of Korea.
| | - Muhammad Torequl Islam
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh.
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13
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Han S, DiBlasi E, Monson ET, Shabalin A, Ferris E, Chen D, Fraser A, Yu Z, Staley M, Callor WB, Christensen ED, Crockett DK, Li QS, Willour V, Bakian AV, Keeshin B, Docherty AR, Eilbeck K, Coon H. Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes. Mol Psychiatry 2023; 28:3909-3919. [PMID: 37794117 PMCID: PMC10730410 DOI: 10.1038/s41380-023-02282-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 09/14/2023] [Accepted: 09/20/2023] [Indexed: 10/06/2023]
Abstract
Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e-06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.
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Affiliation(s)
- Seonggyun Han
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA.
| | - Emily DiBlasi
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Eric T Monson
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Andrey Shabalin
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Elliott Ferris
- Department of Neurobiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Danli Chen
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Alison Fraser
- Pedigree & Population Resource, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Zhe Yu
- Pedigree & Population Resource, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Michael Staley
- Office of the Medical Examiner, Utah Department of Health and Human Services, Salt Lake City, UT, USA
| | - W Brandon Callor
- Office of the Medical Examiner, Utah Department of Health and Human Services, Salt Lake City, UT, USA
| | - Erik D Christensen
- Office of the Medical Examiner, Utah Department of Health and Human Services, Salt Lake City, UT, USA
| | - David K Crockett
- Clinical Analytics, Intermountain Health, Salt Lake City, UT, USA
| | - Qingqin S Li
- Neuroscience Therapeutic Area, Janssen Research & Development, LLC, Titusville, NJ, USA
| | - Virginia Willour
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Amanda V Bakian
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Brooks Keeshin
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Anna R Docherty
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Karen Eilbeck
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Hilary Coon
- Department of Psychiatry & Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
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Payton A, Roell KR, Rebuli ME, Valdar W, Jaspers I, Rager JE. Navigating the bridge between wet and dry lab toxicology research to address current challenges with high-dimensional data. FRONTIERS IN TOXICOLOGY 2023; 5:1171175. [PMID: 37304253 PMCID: PMC10250703 DOI: 10.3389/ftox.2023.1171175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/19/2023] [Indexed: 06/13/2023] Open
Abstract
Toxicology research has rapidly evolved, leveraging increasingly advanced technologies in high-throughput approaches to yield important information on toxicological mechanisms and health outcomes. Data produced through toxicology studies are consequently becoming larger, often producing high-dimensional data. These types of data hold promise for imparting new knowledge, yet inherently have complexities causing them to be a rate-limiting element for researchers, particularly those that are housed in "wet lab" settings (i.e., researchers that use liquids to analyze various chemicals and biomarkers as opposed to more computationally focused, "dry lab" researchers). These types of challenges represent topics of ongoing conversation amongst our team and researchers in the field. The aim of this perspective is to i) summarize hurdles in analyzing high-dimensional data in toxicology that require improved training and translation for wet lab researchers, ii) highlight example methods that have aided in translating data analysis techniques to wet lab researchers; and iii) describe challenges that remain to be effectively addressed, to date, in toxicology research. Specific aspects include methodologies that could be introduced to wet lab researchers, including data pre-processing, machine learning, and data reduction. Current challenges discussed include model interpretability, study biases, and data analysis training. Example efforts implemented to translate these data analysis techniques are also mentioned, including online data analysis resources and hands-on workshops. Questions are also posed to continue conversation in the toxicology community. Contents of this perspective represent timely issues broadly occurring in the fields of bioinformatics and toxicology that require ongoing dialogue between wet and dry lab researchers.
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Affiliation(s)
- Alexis Payton
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Kyle R. Roell
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Meghan E. Rebuli
- Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Curriculum in Toxicology and Environmental Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Pediatrics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - William Valdar
- Department of Genetics, University of North Carolina, Chapel Hill, NC, United States
| | - Ilona Jaspers
- Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Curriculum in Toxicology and Environmental Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Pediatrics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Julia E. Rager
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Curriculum in Toxicology and Environmental Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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15
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Gim JA. Integrative Approaches of DNA Methylation Patterns According to Age, Sex and Longitudinal Changes. Curr Genomics 2023; 23:385-399. [PMID: 37920553 PMCID: PMC10173416 DOI: 10.2174/1389202924666221207100513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 10/04/2022] [Accepted: 11/04/2022] [Indexed: 12/12/2022] Open
Abstract
Background In humans, age-related DNA methylation has been studied in blood, tissues, buccal swabs, and fibroblasts, and changes in DNA methylation patterns according to age and sex have been detected. To date, approximately 137,000 samples have been analyzed from 14,000 studies, and the information has been uploaded to the NCBI GEO database. Methods A correlation between age and methylation level and longitudinal changes in methylation levels was revealed in both sexes. Here, 20 public datasets derived from whole blood were analyzed using the Illumina BeadChip. Batch effects with respect to the time differences were correlated. The overall change in the pattern was provided as the inverse of the coefficient of variation (COV). Results Of the 20 datasets, nine were from a longitudinal study. All data had age and sex as common variables. Comprehensive details of age-, sex-, and longitudinal change-based DNA methylation levels in the whole blood sample were elucidated in this study. ELOVL2 and FHL2 showed the maximum correlation between age and DNA methylation. The methylation patterns of genes related to mental health differed according to age. Age-correlated genes have been associated with malformations (anteverted nostril, craniofacial abnormalities, and depressed nasal bridge) and drug addiction (drug habituation and smoking). Conclusion Based on 20 public DNA methylation datasets, methylation levels according to age and longitudinal changes by sex were identified and visualized using an integrated approach. The results highlight the molecular mechanisms underlying the association of sex and biological age with changes in DNA methylation, and the importance of optimal genomic information management.
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Affiliation(s)
- Jeong-An Gim
- Medical Science Research Center, College of Medicine, Korea University Guro Hospital, Seoul 08308, Republic of Korea
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Sathyanarayanan A, Mueller TT, Ali Moni M, Schueler K, Baune BT, Lio P, Mehta D, Baune BT, Dierssen M, Ebert B, Fabbri C, Fusar-Poli P, Gennarelli M, Harmer C, Howes OD, Janzing JGE, Lio P, Maron E, Mehta D, Minelli A, Nonell L, Pisanu C, Potier MC, Rybakowski F, Serretti A, Squassina A, Stacey D, van Westrhenen R, Xicota L. Multi-omics data integration methods and their applications in psychiatric disorders. Eur Neuropsychopharmacol 2023; 69:26-46. [PMID: 36706689 DOI: 10.1016/j.euroneuro.2023.01.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/22/2022] [Accepted: 01/02/2023] [Indexed: 01/27/2023]
Abstract
To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.
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Affiliation(s)
- Anita Sathyanarayanan
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Tamara T Mueller
- Institute for Artificial Intelligence and Informatics in Medicine, TU Munich, 80333 Munich, Germany
| | - Mohammad Ali Moni
- Artificial Intelligence and Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Katja Schueler
- Clinic for Psychosomatics, Hospital zum Heiligen Geist, Frankfurt am Main, Germany; Frankfurt Psychoanalytic Institute, Frankfurt am Main, Germany
| | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia.
| | | | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Mara Dierssen
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Bjarke Ebert
- Medical Strategy & Communication, H. Lundbeck A/S, Valby, Denmark
| | - Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Intervention and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, King's College London, United Kingdom; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Oliver D Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Psychiatric Imaging, Medical Research Council Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | | | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Eduard Maron
- Department of Psychiatry, University of Tartu, Tartu, Estonia; Centre for Neuropsychopharmacology, Division of Brain Sciences, Imperial College London, London, United Kingdom; Documental Ltd, Tallin, Estonia; West Tallinn Central Hospital, Tallinn, Estonia
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Alessandra Minelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Lara Nonell
- MARGenomics, IMIM (Hospital del Mar Research Institute), Barcelona, Spain
| | - Claudia Pisanu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | | | - Filip Rybakowski
- Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Alessio Squassina
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - David Stacey
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Roos van Westrhenen
- Parnassia Psychiatric Institute, Amsterdam, the Netherlands; Department of Psychiatry and Neuropsychology, Faculty of Health and Sciences, Maastricht University, Maastricht, the Netherlands; Institute of Psychiatry, Psychology & Neuroscience (IoPPN) King's College London, United Kingdom
| | - Laura Xicota
- Paris Brain Institute ICM, Salpetriere Hospital, Paris, France
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17
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Hagenbeek FA, van Dongen J, Pool R, Roetman PJ, Harms AC, Hottenga JJ, Kluft C, Colins OF, van Beijsterveldt CEM, Fanos V, Ehli EA, Hankemeier T, Vermeiren RRJM, Bartels M, Déjean S, Boomsma DI. Integrative Multi-omics Analysis of Childhood Aggressive Behavior. Behav Genet 2023; 53:101-117. [PMID: 36344863 PMCID: PMC9922241 DOI: 10.1007/s10519-022-10126-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 10/25/2022] [Indexed: 11/09/2022]
Abstract
This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N = 277, cases = 42%) and independent clinical data (N = 142, cases = 45%) ranged from 43 to 57%. We observed strong connections between DNA methylation, amino acids, and parental non-transmitted PGSs for ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits link to known and novel risk factors, including inflammation, carcinogens, and smoking.
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Affiliation(s)
- Fiona A. Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-10, 1081 BT Amsterdam, The Netherlands ,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-10, 1081 BT Amsterdam, The Netherlands ,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands ,Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-10, 1081 BT Amsterdam, The Netherlands ,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Peter J. Roetman
- Department of Child and Adolescent Psychiatry, LUMC-Curium, Leiden University Medical Center, Leiden, The Netherlands
| | - Amy C. Harms
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands ,The Netherlands Metabolomics Centre, Leiden, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-10, 1081 BT Amsterdam, The Netherlands
| | | | - Olivier F. Colins
- Department of Child and Adolescent Psychiatry, LUMC-Curium, Leiden University Medical Center, Leiden, The Netherlands ,Department Special Needs Education, Ghent University, Ghent, Belgium
| | | | - Vassilios Fanos
- Department of Surgical Sciences, University of Cagliari and Neonatal Intensive Care Unit, Cagliari, Italy
| | - Erik A. Ehli
- Avera Institute for Human Genetics, Sioux Falls, South Dakota USA
| | - Thomas Hankemeier
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands ,The Netherlands Metabolomics Centre, Leiden, The Netherlands
| | - Robert R. J. M. Vermeiren
- Department of Child and Adolescent Psychiatry, LUMC-Curium, Leiden University Medical Center, Leiden, The Netherlands ,Youz, Parnassia Psychiatric Institute, The Hague, The Netherlands
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-10, 1081 BT Amsterdam, The Netherlands ,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Sébastien Déjean
- Toulouse Mathematics Institute, University of Toulouse, CNRS, Toulouse, France
| | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-10, 1081 BT Amsterdam, The Netherlands ,Amsterdam Public Health Research Institute, Amsterdam, The Netherlands ,Amsterdam Reproduction & Development (AR&D) Research Institute, Amsterdam, The Netherlands
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Zheng Y, Zhang L, He S, Xie Z, Zhang J, Ge C, Sun G, Huang J, Li H. Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) in patients with major depressive disorder: rationale and design of a prospective multicentre cohort study. BMJ Open 2022; 12:e067447. [PMID: 36418119 PMCID: PMC9685190 DOI: 10.1136/bmjopen-2022-067447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION Major depressive disorder (MDD) represents a worldwide burden on healthcare and the response to antidepressants remains limited. Systems biology approaches have been used to explore the precision therapy. However, no reliable biomarker clinically exists for prognostic prediction at present. The objectives of the Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) study are to predict the efficacy of antidepressants by integrating multidimensional omics and performing validation in a real-world setting. As secondary aims, a series of potential biomarkers are explored for biological subtypes. METHODS AND ANALYSIS iMore is an observational cohort study in patients with MDD with a multistage design in China. The study is performed by three mental health centres comprising an observation phase and a validation phase. A total of 200 patients with MDD and 100 healthy controls were enrolled. The protocol-specified antidepressants are selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors. Clinical visits (baseline, 4 and 8 weeks) include psychiatric rating scales for symptom assessment and biospecimen collection for multiomics analysis. Participants are divided into responders and non-responders based on treatment response (>50% reduction in Montgomery-Asberg Depression Rating Scale). Antidepressants' responses are predicted and biomarkers are explored using supervised learning approach by integration of metabolites, cytokines, gut microbiomes and immunophenotypic cells. The accuracy of the prediction models constructed is verified in an independent validation phase. ETHICS AND DISSEMINATION The study was approved by the ethics committee of Shanghai Mental Health Center (approval number 2020-87). All participants need to sign a written consent for the study entry. Study findings will be published in peer-reviewed journals. TRIAL REGISTRATION NUMBER NCT04518592.
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Affiliation(s)
- Yuzhen Zheng
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linna Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shen He
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zuoquan Xie
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Jing Zhang
- Shanghai Green Valley Pharmaceutical Co Ltd, Shanghai, China
| | - Changrong Ge
- Shanghai Green Valley Pharmaceutical Co Ltd, Shanghai, China
| | - Guangqiang Sun
- Shanghai Green Valley Pharmaceutical Co Ltd, Shanghai, China
| | - Jingjing Huang
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center for Mental Health, Shanghai Mental Health Center, Shanghai, China
| | - Huafang Li
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Clinical Research Center for Mental Health, Shanghai Mental Health Center, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Grant CW, Wilton AR, Kaddurah-Daouk R, Skime M, Biernacka J, Mayes T, Carmody T, Wang L, Lazaridis K, Weinshilboum R, Bobo WV, Trivedi MH, Croarkin PE, Athreya AP. Network science approach elucidates integrative genomic-metabolomic signature of antidepressant response and lifetime history of attempted suicide in adults with major depressive disorder. Front Pharmacol 2022; 13:984383. [PMID: 36263124 PMCID: PMC9573988 DOI: 10.3389/fphar.2022.984383] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Individuals with major depressive disorder (MDD) and a lifetime history of attempted suicide demonstrate lower antidepressant response rates than those without a prior suicide attempt. Identifying biomarkers of antidepressant response and lifetime history of attempted suicide may help augment pharmacotherapy selection and improve the objectivity of suicide risk assessments. Towards this goal, this study sought to use network science approaches to establish a multi-omics (genomic and metabolomic) signature of antidepressant response and lifetime history of attempted suicide in adults with MDD. Methods: Single nucleotide variants (SNVs) which associated with suicide attempt(s) in the literature were identified and then integrated with a) p180-assayed metabolites collected prior to antidepressant pharmacotherapy and b) a binary measure of antidepressant response at 8 weeks of treatment using penalized regression-based networks in 245 'Pharmacogenomics Research Network Antidepressant Medication Study (PGRN-AMPS)' and 103 'Combining Medications to Enhance Depression Outcomes (CO-MED)' patients with major depressive disorder. This approach enabled characterization and comparison of biological profiles and associated antidepressant treatment outcomes of those with (N = 46) and without (N = 302) a self-reported lifetime history of suicide attempt. Results: 351 SNVs were associated with suicide attempt(s) in the literature. Intronic SNVs in the circadian genes CLOCK and ARNTL (encoding the CLOCK:BMAL1 heterodimer) were amongst the top network analysis features to differentiate patients with and without a prior suicide attempt. CLOCK and ARNTL differed in their correlations with plasma phosphatidylcholines, kynurenine, amino acids, and carnitines between groups. CLOCK and ARNTL-associated phosphatidylcholines showed a positive correlation with antidepressant response in individuals without a prior suicide attempt which was not observed in the group with a prior suicide attempt. Conclusion: Results provide evidence for a disturbance between CLOCK:BMAL1 circadian processes and circulating phosphatidylcholines, kynurenine, amino acids, and carnitines in individuals with MDD who have attempted suicide. This disturbance may provide mechanistic insights for differential antidepressant pharmacotherapy outcomes between patients with MDD with versus without a lifetime history of attempted suicide. Future investigations of CLOCK:BMAL1 metabolic regulation in the context of suicide attempts may help move towards biologically-augmented pharmacotherapy selection and stratification of suicide risk for subgroups of patients with MDD and a lifetime history of attempted suicide.
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Affiliation(s)
- Caroline W. Grant
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Angelina R. Wilton
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Department of Medicine, Duke Institute for Brain Sciences, Duke University, Durham, NC, United States
| | - Michelle Skime
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Joanna Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
| | - Taryn Mayes
- Peter O’Donnell Jr. Brain Institute and the Department of Psychiatry at the University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Thomas Carmody
- Department Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Konstantinos Lazaridis
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - William V. Bobo
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL, United States
| | - Madhukar H. Trivedi
- Peter O’Donnell Jr. Brain Institute and the Department of Psychiatry at the University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Paul E. Croarkin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Arjun P. Athreya
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
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Jackson NA, Jabbi MM. Integrating biobehavioral information to predict mood disorder suicide risk. Brain Behav Immun Health 2022; 24:100495. [PMID: 35990401 PMCID: PMC9388879 DOI: 10.1016/j.bbih.2022.100495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 11/25/2022] Open
Abstract
The will to live and the ability to maintain one's well-being are crucial for survival. Yet, almost a million people die by suicide globally each year (Aleman and Denys, 2014), making premature deaths due to suicide a significant public health problem (Saxena et al., 2013). The expression of suicidal behaviors is a complex phenotype with documented biological, psychological, clinical, and sociocultural risk factors (Turecki et al., 2019). From a brain disease perspective, suicide is associated with neuroanatomical, neurophysiological, and neurochemical dysregulations of brain networks involved in integrating and contextualizing cognitive and emotional regulatory behaviors. From a symptom perspective, diagnostic measures of dysregulated mood states like major depressive symptoms are associated with over sixty percent of suicide deaths worldwide (Saxena et al., 2013). This paper reviews the neurobiological and clinical phenotypic correlates for mood dysregulations and suicidal phenotypes. We further propose machine learning approaches to integrate neurobiological measures with dysregulated mood symptoms to elucidate the role of inflammatory processes as neurobiological risk factors for suicide.
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Affiliation(s)
- Nicholas A. Jackson
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, USA
- Institute for Neuroscience, The University of Texas at Austin, USA
| | - Mbemba M. Jabbi
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, USA
- Mulva Clinics for the Neurosciences
- Institute for Neuroscience, The University of Texas at Austin, USA
- Department of Psychology, The University of Texas at Austin, USA
- Center for Learning and Memory, The University of Texas at Austin, USA
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21
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Liu S, Lu T, Zhao Q, Fu B, Wang H, Li G, Yang F, Huang J, Lyu N. A machine learning model for predicting patients with major depressive disorder: A study based on transcriptomic data. Front Neurosci 2022; 16:949609. [PMID: 36003956 PMCID: PMC9393475 DOI: 10.3389/fnins.2022.949609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 07/25/2022] [Indexed: 11/19/2022] Open
Abstract
Background Identifying new biomarkers of major depressive disorder (MDD) would be of great significance for its early diagnosis and treatment. Herein, we constructed a diagnostic model of MDD using machine learning methods. Methods The GSE98793 and GSE19738 datasets were obtained from the Gene Expression Omnibus database, and the limma R package was used to analyze differentially expressed genes (DEGs) in MDD patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify potential molecular functions and pathways. A protein-protein interaction network (PPI) was constructed, and hub genes were predicted. Random forest (RF) and artificial neural network (ANN) machine-learning algorithms were used to select variables and construct a robust diagnostic model. Results A total of 721 DEGs were identified in peripheral blood samples of patients with MDD. GO and KEGG analyses revealed that the DEGs were mainly enriched in cytokines, defense responses to viruses, responses to biotic stimuli, immune effector processes, responses to external biotic stimuli, and immune systems. A PPI network was constructed, and CytoHubba plugins were used to screen hub genes. Furthermore, a robust diagnostic model was established using a RF and ANN algorithm with an area under the curve of 0.757 for the training model and 0.685 for the test cohort. Conclusion We analyzed potential driver genes in patients with MDD and built a potential diagnostic model as an adjunct tool to assist psychiatrists in the clinical diagnosis and treatment of MDD.
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Affiliation(s)
- Sitong Liu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Tong Lu
- Department of Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qian Zhao
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Bingbing Fu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Han Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Ginhong Li
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Fan Yang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Juan Huang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Nan Lyu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
- *Correspondence: Nan Lyu,
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22
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Lee KS, Ham BJ. Machine Learning on Early Diagnosis of Depression. Psychiatry Investig 2022; 19:597-605. [PMID: 36059048 PMCID: PMC9441463 DOI: 10.30773/pi.2022.0075] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 05/23/2022] [Indexed: 11/27/2022] Open
Abstract
To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science. The search terms were "depression" (title) and "random forest" (abstract). The eligibility criteria were the dependent variable of depression, the interventions of machine learning (the decision tree, the naïve Bayesian, the random forest, the support vector machine and/or the artificial neural network), the outcomes of accuracy and/or the area under the receiver operating characteristic curve (AUC) for the early diagnosis of depression, the publication year of 2000 or later, the publication language of English and the publication journal of SCIE/SSCI. Different machine learning methods would be appropriate for different types of data for the early diagnosis of depression, e.g., logistic regression, the random forest, the support vector machine and/or the artificial neural network in the case of numeric data, the random forest in the case of genomic data. Their performance measures reported varied within 60.1-100.0 for accuracy and 64.0-96.0 for the AUC. Machine learning provides an effective, non-invasive decision support system for early diagnosis of depression.
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Affiliation(s)
- Kwang-Sig Lee
- AI Center, Korea University Anam Hospital, Seoul, Republic of Korea
| | - Byung-Joo Ham
- Department of Mental Health, Korea University Anam Hospital, Seoul, Republic of Korea
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Mokhtari A, Porte B, Belzeaux R, Etain B, Ibrahim EC, Marie-Claire C, Lutz PE, Delahaye-Duriez A. The molecular pathophysiology of mood disorders: From the analysis of single molecular layers to multi-omic integration. Prog Neuropsychopharmacol Biol Psychiatry 2022; 116:110520. [PMID: 35104608 DOI: 10.1016/j.pnpbp.2022.110520] [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: 10/07/2021] [Revised: 01/22/2022] [Accepted: 01/22/2022] [Indexed: 12/14/2022]
Abstract
Next-generation sequencing now enables the rapid and affordable production of reliable biological data at multiple molecular levels, collectively referred to as "omics". To maximize the potential for discovery, computational biologists have created and adapted integrative multi-omic analytical methods. When applied to diseases with traceable pathophysiology such as cancer, these new algorithms and statistical approaches have enabled the discovery of clinically relevant molecular mechanisms and biomarkers. In contrast, these methods have been much less applied to the field of molecular psychiatry, although diagnostic and prognostic biomarkers are similarly needed. In the present review, we first briefly summarize main findings from two decades of studies that investigated single molecular processes in relation to mood disorders. Then, we conduct a systematic review of multi-omic strategies that have been proposed and used more recently. We also list databases and types of data available to researchers for future work. Finally, we present the newest methodologies that have been employed for multi-omics integration in other medical fields, and discuss their potential for molecular psychiatry studies.
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Affiliation(s)
- Amazigh Mokhtari
- NeuroDiderot, Inserm U1141, Université de Paris, F-75019 Paris, France
| | - Baptiste Porte
- NeuroDiderot, Inserm U1141, Université de Paris, F-75019 Paris, France
| | - Raoul Belzeaux
- Aix Marseille Université CNRS, Institut de Neurosciences de la Timone, F-13005 Marseille, France; Fondation FondaMental, F-94000 Créteil, France; Assistance Publique Hôpitaux de Marseille, Pôle de psychiatrie, pédopsychiatrie et addictologie, F-13005 Marseille, France
| | - Bruno Etain
- Assistance Publique des Hôpitaux de Paris, GHU Lariboisière-Saint Louis-Fernand Widal, DMU Neurosciences, Département de psychiatrie et de Médecine Addictologique, F-75010 Paris, France; Université de Paris, INSERM UMR-S 1144, Optimisation thérapeutique en neuropsychopharmacologie, OTeN, F-75006 Paris, France
| | - El Cherif Ibrahim
- Aix Marseille Université CNRS, Institut de Neurosciences de la Timone, F-13005 Marseille, France
| | - Cynthia Marie-Claire
- Université de Paris, INSERM UMR-S 1144, Optimisation thérapeutique en neuropsychopharmacologie, OTeN, F-75006 Paris, France
| | - Pierre-Eric Lutz
- Centre National de la Recherche Scientifique, Université de Strasbourg, Fédération de Médecine Translationnelle de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives UPR3212, F-67000 Strasbourg, France; Douglas Mental Health University Institute, McGill University, QC H4H 1R3 Montréal, Canada.
| | - Andrée Delahaye-Duriez
- NeuroDiderot, Inserm U1141, Université de Paris, F-75019 Paris, France; Assistance Publique des Hôpitaux de Paris, Unité de médecine génomique, Département BioPhaReS, Hôpital Jean Verdier, Hôpitaux Universitaires de Paris Seine Saint Denis, F-93140 Bondy, France; Université Sorbonne Paris Nord, F-93000 Bobigny, France.
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Gomez Rueda H, Bustillo J. Brain differential gene expression and blood cross-validation of a molecular signature of patients with major depressive disorder. Psychiatr Genet 2022; 32:105-115. [PMID: 35030558 PMCID: PMC9071037 DOI: 10.1097/ypg.0000000000000309] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 11/15/2021] [Indexed: 11/27/2022]
Abstract
INTRODUCTION The agreement between clinicians diagnosing major depressive disorder (MDD) is poor. The objective of this study was to identify a reproducible and robust gene expression marker capable of differentiating MDD from healthy control (HC) subjects. MATERIALS AND METHODS Brain and blood gene expression datasets were searched, which included subjects with MDD and HC. The largest database including different areas of brain samples (GSE80655) was used to identify an initial gene expression marker. Tests of robustness and reproducibility were then implemented in 13 brain and 7 blood independent datasets. Correlations between expression in brain and blood samples were also examined. Finally, an enrichment analysis to explore the marker biological meaning was completed. RESULTS Twenty-eight genes were differentially expressed in GSE80655, of which 23 were critical to differentiate MDD from HC. The accuracy obtained using the 23 genes was 0.77 and 0.8, before and after the forward selection model, respectively. The gene marker's robustness and reproducibility were between the range of 0.46 and 0.63 in the other brain datasets and between 0.45 and 0.78 for the blood datasets. Brain and blood expression tended to correlate in some samples. Thirteen of the 23 genes were related to stress and immune response. CONCLUSION A 23 gene expression marker was able to distinguish subjects with MDD from HC, with adequate reproducibility and low robustness in the independent databases investigated. This gene set was similarly expressed in the brain and blood and involved genes related to stress and immune response.
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Affiliation(s)
- Hugo Gomez Rueda
- Department of Psychiatry and Behavioral Sciences, University of New Mexico Health Sciences Center
| | - Juan Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico Health Sciences Center
- Department of Neurosciences, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA
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Abstract
BACKGROUND In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. METHODS This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis. RESULTS A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test. CONCLUSION The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.
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Affiliation(s)
- Sweta Bhadra
- Department of CS & IT, Cotton University, Guwahati, India
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Lejeune A, Le Glaz A, Perron PA, Sebti J, Baca-Garcia E, Walter M, Lemey C, Berrouiguet S. Artificial intelligence and suicide prevention: a systematic review. Eur Psychiatry 2022; 65:1-22. [PMID: 35166203 PMCID: PMC8988272 DOI: 10.1192/j.eurpsy.2022.8] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/13/2021] [Accepted: 12/20/2021] [Indexed: 11/23/2022] Open
Abstract
Background Suicide is one of the main preventable causes of death. Artificial intelligence (AI) could improve methods for assessing suicide risk. The objective of this review is to assess the potential of AI in identifying patients who are at risk of attempting suicide. Methods A systematic review of the literature was conducted on PubMed, EMBASE, and SCOPUS databases, using relevant keywords. Results Thanks to this research, 296 studies were identified. Seventeen studies, published between 2014 and 2020 and matching inclusion criteria, were selected as relevant. Included studies aimed at predicting individual suicide risk or identifying at-risk individuals in a specific population. The AI performance was overall good, although variable across different algorithms and application settings. Conclusions AI appears to have a high potential for identifying patients at risk of suicide. The precise use of these algorithms in clinical situations, as well as the ethical issues it raises, remain to be clarified.
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Affiliation(s)
- Alban Lejeune
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
| | - Aziliz Le Glaz
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
| | | | - Johan Sebti
- Mental Health Department, French Polynesia Hospital, FFC3+H9G, Pirae, French Polynesia
| | | | - Michel Walter
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- EA 7479 SPURBO, Université de Bretagne Occidentale, Brest, France
| | - Christophe Lemey
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- EA 7479 SPURBO, Université de Bretagne Occidentale, Brest, France
- SPURBO, IMT Atlantique, Lab-STICC, UMR CNRS 6285, F-29238, Brest, France
| | - Sofian Berrouiguet
- URCI Mental Health Department, Brest Medical University Hospital, Brest, France
- LaTIM, INSERM, UMR 1101, Brest, France
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Reiss JD, Peterson LS, Nesamoney SN, Chang AL, Pasca AM, Marić I, Shaw GM, Gaudilliere B, Wong RJ, Sylvester KG, Bonifacio SL, Aghaeepour N, Gibbs RS, Stevenson DK. Perinatal infection, inflammation, preterm birth, and brain injury: A review with proposals for future investigations. Exp Neurol 2022; 351:113988. [DOI: 10.1016/j.expneurol.2022.113988] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 01/06/2022] [Accepted: 01/13/2022] [Indexed: 11/26/2022]
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Wu C, Wang D, Niu K, Feng Q, Chen H, Zhu H, Xiang H. Gene expression profiling in peripheral blood lymphocytes for major depression: preliminary cues from Chinese discordant sib-pair study. Transl Psychiatry 2021; 11:540. [PMID: 34667146 PMCID: PMC8526709 DOI: 10.1038/s41398-021-01665-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 10/05/2021] [Indexed: 12/12/2022] Open
Abstract
The etiology of major depressive disorder (MDD) involves many factors such as heredity and environment. There are very few MDD-related studies in Chinese population using twin or sib-pairs for depression-control samples. Here we used the microarray approach and compared gene expression profiling of peripheral blood lymphocytes from 6 sib-pairs discordant on lifetime history of MDD. Within sib-pair differentially expressed genes are obvious fewer in the 1st, 2nd, and 5th compared with those in the 3rd, 4th, and 6th sib-pairs. Gene expression pattern of these DEGs distinguished MDD individuals from the normal one in 3rd, 4th, and 6th sib-pair but not in the 1st, 2nd, and 5th pair, suggesting heterogeneity of different sib-pairs and somewhat commonalities among the 3rd, 4th, and 6th sib-pairs. Comprehensive protein interaction network analysis revealed two key genes PTH and FGF2 in a dominant network where the majority of the genes were significantly down-regulated. PTH was significantly down-regulated in all the sib-pairs while FGF2 was in the 3rd, 4th, and 6th sib-pairs. KEGG enrichment analysis of all the DEGs in networks showed that PTH and related genes were significantly enriched in the pathway of parathyroid hormone secretion, synthesis, and action while FGF2 and related genes were significantly enriched in the pathways of cancer and specifically breast cancer. Generally reduced expression of these genes in peripheral blood lymphocytes of MDD individuals implied their functional repression associated with MDD. Pending validation in more samples, the findings in this study provided valuable cues for understanding the potential mechanism of MDD, as well as potential markers for the diagnosis and treatment of depression in the Chinese population.
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Affiliation(s)
- Chan Wu
- grid.459864.20000 0004 6005 705XSouth China Normal University-Panyu Central Hospital Joint Laboratory of Basic and Translational Medical Research, Guangzhou Panyu Central Hospital, Guangzhou, China ,grid.263785.d0000 0004 0368 7397Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Guangzhou Key Laboratory of Insect Development Regulation and Application Research, Institute of Insect Science and Technology, School of Life Sciences, South China Normal University, Guangzhou, 510631 China
| | - Danfeng Wang
- grid.410737.60000 0000 8653 1072The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Kangkang Niu
- grid.263785.d0000 0004 0368 7397Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Guangzhou Key Laboratory of Insect Development Regulation and Application Research, Institute of Insect Science and Technology, School of Life Sciences, South China Normal University, Guangzhou, 510631 China
| | - Qili Feng
- grid.459864.20000 0004 6005 705XSouth China Normal University-Panyu Central Hospital Joint Laboratory of Basic and Translational Medical Research, Guangzhou Panyu Central Hospital, Guangzhou, China ,grid.263785.d0000 0004 0368 7397Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Guangzhou Key Laboratory of Insect Development Regulation and Application Research, Institute of Insect Science and Technology, School of Life Sciences, South China Normal University, Guangzhou, 510631 China
| | - Hanwei Chen
- grid.459864.20000 0004 6005 705XSouth China Normal University-Panyu Central Hospital Joint Laboratory of Basic and Translational Medical Research, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Haibing Zhu
- South China Normal University-Panyu Central Hospital Joint Laboratory of Basic and Translational Medical Research, Guangzhou Panyu Central Hospital, Guangzhou, China. .,Department of Psychiatry, Guangzhou Panyu Central Hospital, Guangzhou, China.
| | - Hui Xiang
- South China Normal University-Panyu Central Hospital Joint Laboratory of Basic and Translational Medical Research, Guangzhou Panyu Central Hospital, Guangzhou, China. .,Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Guangzhou Key Laboratory of Insect Development Regulation and Application Research, Institute of Insect Science and Technology, School of Life Sciences, South China Normal University, Guangzhou, 510631, China.
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Kouter K, Videtic Paska A. 'Omics' of suicidal behaviour: A path to personalised psychiatry. World J Psychiatry 2021; 11:774-790. [PMID: 34733641 PMCID: PMC8546767 DOI: 10.5498/wjp.v11.i10.774] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 07/16/2021] [Accepted: 08/30/2021] [Indexed: 02/06/2023] Open
Abstract
Psychiatric disorders, including suicide, are complex disorders that are affected by many different risk factors. It has been estimated that genetic factors contribute up to 50% to suicide risk. As the candidate gene approach has not identified a gene or set of genes that can be defined as biomarkers for suicidal behaviour, much is expected from cutting edge technological approaches that can interrogate several hundred, or even millions, of biomarkers at a time. These include the '-omic' approaches, such as genomics, transcriptomics, epigenomics, proteomics and metabolomics. Indeed, these have revealed new candidate biomarkers associated with suicidal behaviour. The most interesting of these have been implicated in inflammation and immune responses, which have been revealed through different study approaches, from genome-wide single nucleotide studies and the micro-RNA transcriptome, to the proteome and metabolome. However, the massive amounts of data that are generated by the '-omic' technologies demand the use of powerful computational analysis, and also specifically trained personnel. In this regard, machine learning approaches are beginning to pave the way towards personalized psychiatry.
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Affiliation(s)
- Katarina Kouter
- Faculty of Medicine, Institute of Biochemistry and Molecular Genetics, University of Ljubljana, Ljubljana SI-1000, Slovenia
| | - Alja Videtic Paska
- Faculty of Medicine, Institute of Biochemistry and Molecular Genetics, University of Ljubljana, Ljubljana SI-1000, Slovenia
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Dmitrzak-Weglarz M, Szczepankiewicz A, Rybakowski J, Kapelski P, Bilska K, Skibinska M, Reszka E, Lesicka M, Jablonska E, Wieczorek E, Pawlak J. Expression Biomarkers of Pharmacological Treatment Outcomes in Women with Unipolar and Bipolar Depression. PHARMACOPSYCHIATRY 2021; 54:261-268. [PMID: 34470067 DOI: 10.1055/a-1546-9483] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
INTRODUCTION This study aimed to find the expression biomarkers of pharmacological treatment response in a naturalistic hospital setting. Through gene expression profiling, we were able to find differentially-expressed genes (DEGs) in unipolar (UD) and bipolar (BD) depressed women. METHODS We performed gene expression profiling in hospitalized women with unipolar (n=24) and bipolar depression (n=32) who achieved clinical improvement after pharmacological treatment (without any restriction). To identify DEGs in peripheral blood mononuclear cells (PBMCs), we used the SurePrint G3 Microarray and GeneSpring software. RESULTS After pharmacological treatment, UD and BD varied in the number of regulated genes and ontological pathways. Also, the pathways of neurogenesis and synaptic transmission were significantly up-regulated. Our research focused on DEGs with a minimum fold change (FC) of more than 2. For both types of depression, 2 up-regulated genes, OPRM1 and CELF4 (p=0.013), were significantly associated with treatment response (defined as a 50% reduction on the Hamilton Depression Rating Scale [HDRS]). We also uncovered the SHANK3 (p=0.001) gene that is unique for UD and found that the RASGRF1 (p=0.010) gene may be a potential specific biomarker of treatment response for BD. CONCLUSION Based on transcriptomic profiling, we identified potential expression biomarkers of treatment outcomes for UD and BD. We also proved that the Ras-GEF pathway associated with long-term memory, female stress response, and treatment response modulation in animal studies impacts treatment efficacy in patients with BD. Further studies focused on the outlined genes may help provide predictive markers of treatment outcomes in UD and BD.
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Affiliation(s)
| | - Aleksandra Szczepankiewicz
- Laboratory of Molecular and Cell Biology, Department of Pediatric Pulmonology, Allergy and Clinical Immunology, Poznan University of Medical Sciences, Poland
| | - Janusz Rybakowski
- Department of Adult Psychiatry, Poznan University of Medical Sciences, Poland
| | - Paweł Kapelski
- Department of Psychiatric Genetics, Poznan University of Medical Sciences, Poland
| | - Karolina Bilska
- Department of Psychiatric Genetics, Poznan University of Medical Sciences, Poland
| | - Maria Skibinska
- Department of Psychiatric Genetics, Poznan University of Medical Sciences, Poland
| | - Edyta Reszka
- Department of Molecular Genetics and Epigenetics, Nofer Institute of Occupational Medicine, Lodz, Poland
| | - Monika Lesicka
- Department of Molecular Genetics and Epigenetics, Nofer Institute of Occupational Medicine, Lodz, Poland
| | - Ewa Jablonska
- Department of Molecular Genetics and Epigenetics, Nofer Institute of Occupational Medicine, Lodz, Poland
| | - Edyta Wieczorek
- Department of Molecular Genetics and Epigenetics, Nofer Institute of Occupational Medicine, Lodz, Poland
| | - Joanna Pawlak
- Department of Psychiatric Genetics, Poznan University of Medical Sciences, Poland
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Videtič Paska A, Kouter K. Machine learning as the new approach in understanding biomarkers of suicidal behavior. Bosn J Basic Med Sci 2021; 21:398-408. [PMID: 33485296 PMCID: PMC8292863 DOI: 10.17305/bjbms.2020.5146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 12/15/2020] [Indexed: 11/16/2022] Open
Abstract
In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable more objective and faster clinical diagnosis, implement accurate monitoring of treatment response and remission, is grave. Current technological development enables analyses of various biological marks in high throughput scale at reasonable costs, and therefore 'omic' studies are entering the psychiatry research. However, big data demands a whole new plethora of skills in data processing, before clinically useful information can be extracted. So far the classical approach to data analysis did not really contribute to identification of biomarkers in psychiatry, but the extensive amounts of data might get to a higher level, if artificial intelligence in the shape of machine learning algorithms would be applied. Not many studies on machine learning in psychiatry have been published, but we can already see from that handful of studies that the potential to build a screening portfolio of biomarkers for different psychopathologies, including suicide, exists.
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Affiliation(s)
- Alja Videtič Paska
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Katarina Kouter
- Medical Centre for Molecular Biology, Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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He L, Luo L, Hou X, Liao D, Liu R, Ouyang C, Wang G. Predicting venous thromboembolism in hospitalized trauma patients: a combination of the Caprini score and data-driven machine learning model. BMC Emerg Med 2021; 21:60. [PMID: 33971809 PMCID: PMC8111727 DOI: 10.1186/s12873-021-00447-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 04/06/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Venous thromboembolism (VTE) is a common complication of hospitalized trauma patients and has an adverse impact on patient outcomes. However, there is still a lack of appropriate tools for effectively predicting VTE for trauma patients. We try to verify the accuracy of the Caprini score for predicting VTE in trauma patients, and further improve the prediction through machine learning algorithms. METHODS We retrospectively reviewed emergency trauma patients who were admitted to a trauma center in a tertiary hospital from September 2019 to March 2020. The data in the patient's electronic health record (EHR) and the Caprini score were extracted, combined with multiple feature screening methods and the random forest (RF) algorithm to constructs the VTE prediction model, and compares the prediction performance of (1) using only Caprini score; (2) using EHR data to build a machine learning model; (3) using EHR data and Caprini score to build a machine learning model. True Positive Rate (TPR), False Positive Rate (FPR), Area Under Curve (AUC), accuracy, and precision were reported. RESULTS The Caprini score shows a good VTE prediction effect on the trauma hospitalized population when the cut-off point is 11 (TPR = 0.667, FPR = 0.227, AUC = 0.773), The best prediction model is LASSO+RF model combined with Caprini Score and other five features extracted from EHR data (TPR = 0.757, FPR = 0.290, AUC = 0.799). CONCLUSION The Caprini score has good VTE prediction performance in trauma patients, and the use of machine learning methods can further improve the prediction performance.
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Affiliation(s)
- Lingxiao He
- Trauma Center of West China Hospital/West China School of Nursing, Sichuan University, Guo Xue Road 37#, Chengdu, 610041, China
| | - Lei Luo
- College of Chemical Engineering, Sichuan University, Chengdu, China
| | - Xiaoling Hou
- Trauma Center of West China Hospital/West China School of Nursing, Sichuan University, Guo Xue Road 37#, Chengdu, 610041, China
| | - Dengbin Liao
- Trauma Center of West China Hospital/West China School of Nursing, Sichuan University, Guo Xue Road 37#, Chengdu, 610041, China
| | - Ran Liu
- Engineering Research Center of Medical Information Technology, Ministry of Education, West China Hospital of Sichuan University, Chengdu, China
| | - Chaowei Ouyang
- Trauma Center of West China Hospital/West China School of Nursing, Sichuan University, Guo Xue Road 37#, Chengdu, 610041, China
| | - Guanglin Wang
- Trauma Center of West China Hospital/West China School of Medicine, Sichuan University, Guo Xue Road 37#, Chengdu, 610041, China.
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33
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Tang M, Liu T, Jiang P, Dang R. The interaction between autophagy and neuroinflammation in major depressive disorder: From pathophysiology to therapeutic implications. Pharmacol Res 2021; 168:105586. [PMID: 33812005 DOI: 10.1016/j.phrs.2021.105586] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/19/2021] [Accepted: 03/25/2021] [Indexed: 12/14/2022]
Abstract
The past decade has revealed neuroinflammation as an important mechanism of major depressive disorder (MDD). Nod-like receptors family pyrin domain containing 3 (NLRP3) inflammasome is the key regulator interleukin-1β (IL-1β) maturation, whose activation has been reported in MDD patients and various animal models. Function as a dominant driver of neuroinflammation, NLRP3 bridges the gap between immune activation with stress exposure, and further leads to subsequent occurrence of neuropsychiatric disorders such as MDD. Of note, autophagy is a tightly regulated cellular degradation pathway that removes damaged organelles and intracellular pathogens, and maintains cellular homeostasis from varying insults. Serving as a critical cellular monitoring system, normal functioned autophagy signaling prevents excessive NLRP3 inflammasome activation and subsequent release of IL-1 family cytokines. This review will describe the current understanding of how autophagy regulates NLRP3 inflammasome activity and discuss the implications of this regulation on the pathogenesis of MDD. The extensive crosstalk between autophagy pathway and NLRP3 inflammasome is further discussed, as it is critical for developing new therapeutic strategies for MDD aimed at modulating the neuroinflammatory responses.
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Affiliation(s)
- Mimi Tang
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Ting Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Pei Jiang
- Institute of Clinical Pharmacy, Jining First People's Hospital, Jining Medical University, Jining 272000, China.
| | - Ruili Dang
- Institute of Clinical Pharmacy, Jining First People's Hospital, Jining Medical University, Jining 272000, China.
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34
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Qi B, Ramamurthy J, Bennani I, Trakadis YJ. Machine learning and bioinformatic analysis of brain and blood mRNA profiles in major depressive disorder: A case-control study. Am J Med Genet B Neuropsychiatr Genet 2021; 186:101-112. [PMID: 33645908 DOI: 10.1002/ajmg.b.32839] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 01/08/2021] [Accepted: 02/03/2021] [Indexed: 12/13/2022]
Abstract
This study analyzed gene expression messenger RNA data, from cases with major depressive disorder (MDD) and controls, using supervised machine learning (ML). We built on the methodology of prior studies to obtain more generalizable/reproducible results. First, we obtained a classifier trained on gene expression data from the dorsolateral prefrontal cortex of post-mortem MDD cases (n = 126) and controls (n = 103). An average area-under-the-receiver-operating-characteristics-curve (AUC) from 10-fold cross-validation of 0.72 was noted, compared to an average AUC of 0.55 for a baseline classifier (p = .0048). The classifier achieved an AUC of 0.76 on a previously unused testing-set. We also performed external validation using DLPFC gene expression values from an independent cohort of matched MDD cases (n = 29) and controls (n = 29), obtained from Affymetrix microarray (vs. Illumina microarray for the original cohort) (AUC: 0.62). We highlighted gene sets differentially expressed in MDD that were enriched for genes identified by the ML algorithm. Next, we assessed the ML classification performance in blood-based microarray gene expression data from MDD cases (n = 1,581) and controls (n = 369). We observed a mean AUC of 0.64 on 10-fold cross-validation, which was significantly above baseline (p = .0020). Similar performance was observed on the testing-set (AUC: 0.61). Finally, we analyzed the classification performance in covariates subgroups. We identified an interesting interaction between smoking and recall performance in MDD case prediction (58% accurate predictions in cases who are smokers vs. 43% accurate predictions in cases who are non-smokers). Overall, our results suggest that ML in combination with gene expression data and covariates could further our understanding of the pathophysiology in MDD.
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Affiliation(s)
- Bill Qi
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | | | - Imane Bennani
- Faculty of Science, McGill University, Montreal, Quebec, Canada
| | - Yannis J Trakadis
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.,Department of Medical Genetics, McGill University Health Center, Montreal, Quebec, Canada
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Bai S, Fang L, Xie J, Bai H, Wang W, Chen JJ. Potential Biomarkers for Diagnosing Major Depressive Disorder Patients with Suicidal Ideation. J Inflamm Res 2021; 14:495-503. [PMID: 33654420 PMCID: PMC7910095 DOI: 10.2147/jir.s297930] [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: 12/18/2020] [Accepted: 02/03/2021] [Indexed: 12/15/2022] Open
Abstract
Background Major depressive disorder (MDD) and suicide are two major health problems, but there are still no objective methods to diagnose MDD or suicidal ideation (SI). This study was conducted to identify potential biomarkers for diagnosing MDD patients with SI. Methods First-episode drug-naïve MDD patients with SI and demographics-matched healthy controls (HCs) were recruited. First-episode drug-naïve MDD patients without SI were also included. The serum lipids, C-reactive protein (CRP), transferring (TRSF), homocysteine (HCY) and alpha 1-antitrypsin (AAT) in serum were detected. The univariate and multivariate statistical analyses were used to identify and validate the potential biomarkers. Results The 86 HCs, 53 MDD patients with SI and 20 MDD patients without SI were included in this study. Four potential biomarkers were identified: AAT, TRSF, high-density lipoprotein cholesterol (HDLC), and apolipoprotein A1 (APOA1). After one month treatment, the levels of AAT and APOA1 were significantly improved. The panel consisting of these potential biomarkers had an excellent diagnostic performance, yielding an area under the ROC curve (AUC) of 0.994 and 0.990 in the training and testing set, respectively. Moreover, this panel could effectively distinguish MDD patients with SI from MDD patients without SI (AUC=0.928). Conclusion These results showed that these potential biomarkers could facilitate the development of an objective method for diagnosing MDD patients with SI, and the decreased AAT levels in MDD patients might lead to the appearance of SI by resulting in the elevated inflammation.
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Affiliation(s)
- Shunjie Bai
- Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Liang Fang
- Department of Neurology, Yongchuan Hospital of Chongqing Medical University, Chongqing, People's Republic of China.,Chongqing Key Laboratory of Cerebral Vascular Disease Research, Yongchuan Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Jing Xie
- Department of Endocrinology and Nephrology, The Fourth People's Hospital of Chongqing, Chongqing, People's Republic of China
| | - Huili Bai
- Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Wei Wang
- Department of Neurology, The Affiliated Hospital of Guizhou Medical University, Guiyang, People's Republic of China
| | - Jian-Jun Chen
- Institute of Life Sciences, Chongqing Medical University, Chongqing, People's Republic of China
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Sani G, Manchia M, Simonetti A, Janiri D, Paribello P, Pinna F, Carpiniello B. The Role of Gut Microbiota in the High-Risk Construct of Severe Mental Disorders: A Mini Review. Front Psychiatry 2021; 11:585769. [PMID: 33510657 PMCID: PMC7835325 DOI: 10.3389/fpsyt.2020.585769] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 12/15/2020] [Indexed: 12/20/2022] Open
Abstract
Severe mental disorders (SMD) are highly prevalent psychiatric conditions exerting an enormous toll on society. Therefore, prevention of SMD has received enormous attention in the last two decades. Preventative approaches are based on the knowledge and detailed characterization of the developmental stages of SMD and on risk prediction. One relevant biological component, so far neglected in high risk research, is microbiota. The human microbiota consists in the ensemble of microbes, including viruses, bacteria, and eukaryotes, that inhabit several ecological niches of the organism. Due to its demonstrated role in modulating illness and health, as well in influencing behavior, much interest has focused on the characterization of the microbiota inhabiting the gut. Several studies in animal models have shown the early modifications in the gut microbiota might impact on neurodevelopment and the onset of deficits in social behavior corresponding to distinct neurosignaling alterations. However, despite this evidence, only one study investigated the effect of altered microbiome and risk of developing mental disorders in humans, showing that individuals at risk for SMD had significantly different global microbiome composition than healthy controls. We then offer a developmental perspective and provided mechanistic insights on how changes in the microbiota could influence the risk of SMD. We suggest that the analysis of microbiota should be included in the comprehensive assessment generally performed in populations at high risk for SMD as it can inform predictive models and ultimately preventative strategies.
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Affiliation(s)
- Gabriele Sani
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di ricovero e cura a carattere scientifico (IRCCS), Rome, Italy
- Section of Psychiatry, Department of Neuroscience, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
- Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | - Alessio Simonetti
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
- Department of Neurology and Psychiatry, Sapienza University of Rome, Rome, Italy
| | - Delfina Janiri
- Fondazione Policlinico Universitario “Agostino Gemelli” Istituto di ricovero e cura a carattere scientifico (IRCCS), Rome, Italy
- Section of Psychiatry, Department of Neuroscience, Università Cattolica del Sacro Cuore, Rome, Italy
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Pasquale Paribello
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
| | - Federica Pinna
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
| | - Bernardo Carpiniello
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
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Polygenic risk scores for genetic counseling in psychiatry: Lessons learned from other fields of medicine. Neurosci Biobehav Rev 2020; 121:119-127. [PMID: 33301779 DOI: 10.1016/j.neubiorev.2020.11.021] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 11/17/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022]
Abstract
Polygenic risk scores (PRS) may aid in the identification of individuals at-risk for psychiatric disorders, treatment optimization, and increase in prognostic accuracy. PRS may also add significant value to genetic counseling. Thus far, integration of PRSs in genetic counseling sessions remains problematic because of uncertainties in risk prediction and other concerns. Here, we review the current utility of PRSs in the context of clinical psychiatry. By comprehensively appraising the literature in other fields of medicine including breast cancer, Alzheimer's Disease, and cardiovascular disease, we outline several lessons learned that could be applied to future studies and may thus benefit the incorporation of PRS in psychiatric genetic counseling. These include integrating PRS with environmental factors (e.g. lifestyle), setting up large-scale studies, and applying reproducible methods allowing for cross-validation between cohorts. We conclude that psychiatry may benefit from experiences in these fields. PRS may in future have a role in genetic counseling in clinical psychiatric practice, by advancing prevention strategies and treatment decision-making, thus promoting quality of life for (potentially) affected individuals.
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Fontana A, Manchia M, Panebianco C, Paribello P, Arzedi C, Cossu E, Garzilli M, Montis MA, Mura A, Pisanu C, Congiu D, Copetti M, Pinna F, Carpiniello B, Squassina A, Pazienza V. Exploring the Role of Gut Microbiota in Major Depressive Disorder and in Treatment Resistance to Antidepressants. Biomedicines 2020; 8:biomedicines8090311. [PMID: 32867257 PMCID: PMC7554953 DOI: 10.3390/biomedicines8090311] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 08/24/2020] [Accepted: 08/25/2020] [Indexed: 12/24/2022] Open
Abstract
Major depressive disorder (MDD) is a common severe psychiatric illness, exhibiting sub-optimal response to existing pharmacological treatments. Although its etiopathogenesis is still not completely understood, recent findings suggest that an altered composition of the gut microbiota might play a role. Here we aimed to explore potential differences in the composition of the gut microbiota between patients with MDD and healthy controls (HC) and to identify possible signatures of treatment response by analyzing two groups of MDD patients characterized as treatment-resistant (TR) or responders (R) to antidepressants. Stool samples were collected from 34 MDD patients (8 TR, 19 R and 7 untreated) and 20 HC. Microbiota was characterized using the 16S metagenomic approach. A penalized logistic regression analysis algorithm was applied to identify bacterial populations that best discriminate the diagnostic groups. Statistically significant differences were identified for the families of Paenibacillaceae and Flavobacteriaceaea, for the genus Fenollaria, and the species Flintibacter butyricus, Christensenella timonensis, and Eisenbergiella massiliensis among others. The phyla Proteobacteria, Tenericutes and the family Peptostreptococcaceae were more abundant in TR, whereas the phylum Actinobacteria was enriched in R patients. Moreover, a number of bacteria only characterized the microbiota of TR patients, and many others were only detected in R. Our results confirm that dysbiosis is a hallmark of MDD and suggest that microbiota of TR patients significantly differs from responders to antidepressants. This finding further supports the relevance of an altered composition of the gut microbiota in the etiopathogenesis of MDD, suggesting a role in response to antidepressants.
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Affiliation(s)
- Andrea Fontana
- Unit of Biostatistics, Fondazione IRCCS Casa Sollievo della Sofferenza Hospital, 71013 San Giovanni Rotondo, Italy; (A.F.); (M.C.)
| | - Mirko Manchia
- Unit of Psychiatry, Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, 09042 Cagliari, Italy; (M.M.); (P.P.); (C.A.); (E.C.); (M.G.); (M.A.M.); (A.M.); (F.P.); (B.C.)
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, 09042 Cagliari, Italy
- Department of Pharmacology, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Concetta Panebianco
- Division of Gastroenterology, Fondazione IRCCS Casa Sollievo della Sofferenza Hospital, 71013 San Giovanni Rotondo, Italy;
| | - Pasquale Paribello
- Unit of Psychiatry, Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, 09042 Cagliari, Italy; (M.M.); (P.P.); (C.A.); (E.C.); (M.G.); (M.A.M.); (A.M.); (F.P.); (B.C.)
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, 09042 Cagliari, Italy
| | - Carlo Arzedi
- Unit of Psychiatry, Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, 09042 Cagliari, Italy; (M.M.); (P.P.); (C.A.); (E.C.); (M.G.); (M.A.M.); (A.M.); (F.P.); (B.C.)
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, 09042 Cagliari, Italy
| | - Eleonora Cossu
- Unit of Psychiatry, Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, 09042 Cagliari, Italy; (M.M.); (P.P.); (C.A.); (E.C.); (M.G.); (M.A.M.); (A.M.); (F.P.); (B.C.)
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, 09042 Cagliari, Italy
| | - Mario Garzilli
- Unit of Psychiatry, Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, 09042 Cagliari, Italy; (M.M.); (P.P.); (C.A.); (E.C.); (M.G.); (M.A.M.); (A.M.); (F.P.); (B.C.)
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, 09042 Cagliari, Italy
| | - Maria Antonietta Montis
- Unit of Psychiatry, Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, 09042 Cagliari, Italy; (M.M.); (P.P.); (C.A.); (E.C.); (M.G.); (M.A.M.); (A.M.); (F.P.); (B.C.)
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, 09042 Cagliari, Italy
| | - Andrea Mura
- Unit of Psychiatry, Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, 09042 Cagliari, Italy; (M.M.); (P.P.); (C.A.); (E.C.); (M.G.); (M.A.M.); (A.M.); (F.P.); (B.C.)
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, 09042 Cagliari, Italy
| | - Claudia Pisanu
- Unit of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, 09042 Cagliari, Italy; (C.P.); (D.C.)
| | - Donatella Congiu
- Unit of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, 09042 Cagliari, Italy; (C.P.); (D.C.)
| | - Massimiliano Copetti
- Unit of Biostatistics, Fondazione IRCCS Casa Sollievo della Sofferenza Hospital, 71013 San Giovanni Rotondo, Italy; (A.F.); (M.C.)
| | - Federica Pinna
- Unit of Psychiatry, Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, 09042 Cagliari, Italy; (M.M.); (P.P.); (C.A.); (E.C.); (M.G.); (M.A.M.); (A.M.); (F.P.); (B.C.)
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, 09042 Cagliari, Italy
| | - Bernardo Carpiniello
- Unit of Psychiatry, Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, 09042 Cagliari, Italy; (M.M.); (P.P.); (C.A.); (E.C.); (M.G.); (M.A.M.); (A.M.); (F.P.); (B.C.)
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, 09042 Cagliari, Italy
| | - Alessio Squassina
- Unit of Neuroscience and Clinical Pharmacology, Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, 09042 Cagliari, Italy; (C.P.); (D.C.)
- Department of Biomedical Sciences, Division of Neuroscience and Clinical Pharmacology, University Campus, S.P. 8, Sestu-Monserrato, Km 0.700, Monserrato, 09042 Cagliari, Italy
- Correspondence: (A.S.); (V.P.)
| | - Valerio Pazienza
- Division of Gastroenterology, Fondazione IRCCS Casa Sollievo della Sofferenza Hospital, 71013 San Giovanni Rotondo, Italy;
- Gastroenterology Unit, Fondazione I.R.C.C.S. “Casa Sollievo della Sofferenza” Hospital, Viale dei Cappuccini 1, 71013 San Giovanni Rotondo, Italy
- Correspondence: (A.S.); (V.P.)
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