1
|
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.
Collapse
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.
| |
Collapse
|
2
|
Okpete UE, Byeon H. Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment. World J Psychiatry 2024; 14:1148-1164. [DOI: 10.5498/wjp.v14.i8.1148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/13/2024] [Accepted: 07/09/2024] [Indexed: 08/12/2024] Open
Abstract
Precision medicine is transforming psychiatric treatment by tailoring personalized healthcare interventions based on clinical, genetic, environmental, and lifestyle factors to optimize medication management. This study investigates how artificial intelligence (AI) and machine learning (ML) can address key challenges in integrating pharmacogenomics (PGx) into psychiatric care. In this integration, AI analyzes vast genomic datasets to identify genetic markers linked to psychiatric conditions. AI-driven models integrating genomic, clinical, and demographic data demonstrated high accuracy in predicting treatment outcomes for major depressive disorder and bipolar disorder. This study also examines the pressing challenges and provides strategic directions for integrating AI and ML in genomic psychiatry, highlighting the importance of ethical considerations and the need for personalized treatment. Effective implementation of AI-driven clinical decision support systems within electronic health records is crucial for translating PGx into routine psychiatric care. Future research should focus on developing enhanced AI-driven predictive models, privacy-preserving data exchange, and robust informatics systems to optimize patient outcomes and advance precision medicine in psychiatry.
Collapse
Affiliation(s)
- Uchenna Esther Okpete
- Department of Digital Anti-aging Healthcare (BK21), Inje University, Gimhae 50834, South Korea
| | - Haewon Byeon
- Department of Digital Anti-aging Healthcare (BK21), Inje University, Gimhae 50834, South Korea
- Department of Medical Big Data, Inje University, Gimhae 50834, South Korea
| |
Collapse
|
3
|
Dickinson H, Teltsch DY, Feifel J, Hunt P, Vallejo-Yagüe E, Virkud AV, Muylle KM, Ochi T, Donneyong M, Zabinski J, Strauss VY, Hincapie-Castillo JM. The Unseen Hand: AI-Based Prescribing Decision Support Tools and the Evaluation of Drug Safety and Effectiveness. Drug Saf 2024; 47:117-123. [PMID: 38019365 DOI: 10.1007/s40264-023-01376-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/26/2023] [Indexed: 11/30/2023]
Abstract
The use of artificial intelligence (AI)-based tools to guide prescribing decisions is full of promise and may enhance patient outcomes. These tools can perform actions such as choosing the 'safest' medication, choosing between competing medications, promoting de-prescribing or even predicting non-adherence. These tools can exist in a variety of formats; for example, they may be directly integrated into electronic medical records or they may exist in a stand-alone website accessible by a web browser. One potential impact of these tools is that they could manipulate our understanding of the benefit-risk of medicines in the real world. Currently, the benefit risk of approved medications is assessed according to carefully planned agreements covering spontaneous reporting systems and planned surveillance studies. But AI-based tools may limit or even block prescription to high-risk patients or prevent off-label use. The uptake and temporal availability of these tools may be uneven across healthcare systems and geographies, creating artefacts in data that are difficult to account for. It is also hard to estimate the 'true impact' that a tool had on a prescribing decision. International borders may also be highly porous to these tools, especially in cases where tools are available over the web. These tools already exist, and their use is likely to increase in the coming years. How they can be accounted for in benefit-risk decisions is yet to be seen.
Collapse
Affiliation(s)
| | | | - Jan Feifel
- Merck Healthcare KGaA, Darmstadt, Germany
| | - Philip Hunt
- Institute of Pharmaceutical Sciences, ETH Zurich, Zurich, Switzerland
| | - Enriqueta Vallejo-Yagüe
- AstraZeneca, Gaithersberg, MD, USA
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
| | - Arti V Virkud
- Kidney Center School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Taichi Ochi
- Department of PharmacoTherapy, Epidemiology and Economics, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands
- Center for Innovation in Medicine, Bucharest, Romania
| | | | | | - Victoria Y Strauss
- Boehringer Ingelheim, Binger Str. 173, 55218, Ingelheim am Rhein, Germany
| | | |
Collapse
|
4
|
Squires M, Tao X, Elangovan S, Gururajan R, Zhou X, Li Y, Acharya UR. Identifying predictive biomarkers for repetitive transcranial magnetic stimulation response in depression patients with explainability. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107771. [PMID: 37717523 DOI: 10.1016/j.cmpb.2023.107771] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/12/2023] [Accepted: 08/19/2023] [Indexed: 09/19/2023]
Abstract
Repetitive Transcranial Magnetic Stimulation (rTMS) is an evidence-based treatment for depression. However, the patterns of response to this treatment modality are inconsistent. Whilst many people see a significant reduction in the severity of their depression following rTMS treatment, some patients do not. To support and improve patient outcomes, recent work is exploring the possibility of using Machine Learning to predict rTMS treatment outcomes. Our proposed model is the first to combine functional magnetic resonance imaging (fMRI) connectivity with deep learning techniques to predict treatment outcomes before treatment starts. Furthermore, with the use of Explainable AI (XAI) techniques, we identify potential biomarkers that may discriminate between rTMS responders and non-responders. Our experiments utilize 200 runs of repeated bootstrap sampling on two rTMS datasets. We compare performances between our proposed feedforward deep neural network against existing methods, and compare the average accuracy, balanced accuracy and F1-score on a held-out test set. The results of these experiments show that our model outperforms existing methods with an average accuracy of 0.9423, balanced accuracy of 0.9423, and F1-score of 0.9461 in a sample of 61 patients. We found that functional connectivity measures between the Subgenual Anterior Cingulate Cortex and Centeral Opercular Cortex are a key determinant of rTMS treatment response. This knowledge provides psychiatrists with further information to explore the potential mechanisms of responses to rTMS treatment. Our developed prototype is ready to be deployed across large datasets in multiple centres and different countries.
Collapse
Affiliation(s)
- Matthew Squires
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia.
| | - Xiaohui Tao
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Australia.
| | | | - Raj Gururajan
- School of Business, University of Southern Queensland, Springfield, Australia.
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, Springfield, Australia.
| | - Yuefeng Li
- School of Computer Science, Queensland University of Technology, Brisbane, Australia.
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia.
| |
Collapse
|
5
|
Nicolini ME, Jardas EJ, Zarate CA, Gastmans C, Kim SYH. Irremediability in psychiatric euthanasia: examining the objective standard. Psychol Med 2023; 53:5729-5747. [PMID: 36305567 PMCID: PMC10482705 DOI: 10.1017/s0033291722002951] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/22/2022] [Accepted: 08/31/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Irremediability is a key requirement for euthanasia and assisted suicide for psychiatric disorders (psychiatric EAS). Countries like the Netherlands and Belgium ask clinicians to assess irremediability in light of the patient's diagnosis and prognosis and 'according to current medical understanding'. Clarifying the relevance of a default objective standard for irremediability when applied to psychiatric EAS is crucial for solid policymaking. Yet so far, a thorough examination of this standard is lacking. METHODS Using treatment-resistant depression (TRD) as a test case, through a scoping review in PubMed, we analyzed the state-of-the-art evidence for whether clinicians can accurately predict individual long-term outcome and single out irremediable cases, by examining the following questions: (1) What is the definition of TRD; (2) What are group-level long-term outcomes of TRD; and (3) Can clinicians make accurate individual outcome predictions in TRD? RESULTS A uniform definition of TRD is lacking, with over 150 existing definitions, mostly focused on psychopharmacological research. Available yet limited studies about long-term outcomes indicate that a majority of patients with long-term TRD show significant improvement over time. Finally, evidence about individual predictions in TRD using precision medicine is growing, but methodological shortcomings and varying predictive accuracies pose important challenges for its implementation in clinical practice. CONCLUSION Our findings support the claim that, as per available evidence, clinicians cannot accurately predict long-term chances of recovery in a particular patient with TRD. This means that the objective standard for irremediability cannot be met, with implications for policy and practice of psychiatric EAS.
Collapse
Affiliation(s)
- Marie E Nicolini
- Department of Bioethics, National Institutes of Health, 10 Center Drive, Room 1C118, Bethesda, Maryland 20892, USA
- Center for Biomedical Ethics and Law, KU Leuven, Kapucijnenvoer 35 - Box 7001, 3000 Leuven, Belgium
| | - E J Jardas
- Department of Bioethics, National Institutes of Health, 10 Center Drive, Room 1C118, Bethesda, Maryland 20892, USA
| | - Carlos A Zarate
- Section on the Neurobiology and Treatment of Mood Disorders, Experimental Therapeutics and Pathophysiology Branch, National Institutes of Mental Health, 6001 Executive Boulevard, Room 6200, MSC 9663, Bethesda, MD 20892, USA
| | - Chris Gastmans
- Center for Biomedical Ethics and Law, KU Leuven, Kapucijnenvoer 35 - Box 7001, 3000 Leuven, Belgium
| | - Scott Y H Kim
- Department of Bioethics, National Institutes of Health, 10 Center Drive, Room 1C118, Bethesda, Maryland 20892, USA
| |
Collapse
|
6
|
Kim JS, Wang B, Kim M, Lee J, Kim H, Roh D, Lee KH, Hong SB, Lim JS, Kim JW, Ryan N. Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study. JMIR Form Res 2023; 7:e45991. [PMID: 37223978 DOI: 10.2196/45991] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/25/2023] [Accepted: 04/18/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Lack of quantifiable biomarkers is a major obstacle in diagnosing and treating depression. In adolescents, increasing suicidality during antidepressant treatment further complicates the problem. OBJECTIVE We sought to evaluate digital biomarkers for the diagnosis and treatment response of depression in adolescents through a newly developed smartphone app. METHODS We developed the Smart Healthcare System for Teens At Risk for Depression and Suicide app for Android-based smartphones. This app passively collected data reflecting the social and behavioral activities of adolescents, such as their smartphone usage time, physical movement distance, and the number of phone calls and text messages during the study period. Our study consisted of 24 adolescents (mean age 15.4 [SD 1.4] years, 17 girls) with major depressive disorder (MDD) diagnosed with Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version and 10 healthy controls (mean age 13.8 [SD 0.6] years, 5 girls). After 1 week's baseline data collection, adolescents with MDD were treated with escitalopram in an 8-week, open-label trial. Participants were monitored for 5 weeks, including the baseline data collection period. Their psychiatric status was measured every week. Depression severity was measured using the Children's Depression Rating Scale-Revised and Clinical Global Impressions-Severity. The Columbia Suicide Severity Rating Scale was administered in order to assess suicide severity. We applied the deep learning approach for the analysis of the data. Deep neural network was employed for diagnosis classification, and neural network with weighted fuzzy membership functions was used for feature selection. RESULTS We could predict the diagnosis of depression with training accuracy of 96.3% and 3-fold validation accuracy of 77%. Of the 24 adolescents with MDD, 10 responded to antidepressant treatments. We predicted the treatment response of adolescents with MDD with training accuracy of 94.2% and 3-fold validation accuracy of 76%. Adolescents with MDD tended to move longer distances and use smartphones for longer periods of time compared to controls. The deep learning analysis showed that smartphone usage time was the most important feature in distinguishing adolescents with MDD from controls. Prominent differences were not observed in the pattern of each feature between the treatment responders and nonresponders. The deep learning analysis revealed that the total length of calls received as the most important feature predicting antidepressant response in adolescents with MDD. CONCLUSIONS Our smartphone app demonstrated preliminary evidence of predicting diagnosis and treatment response in depressed adolescents. This is the first study to predict the treatment response of adolescents with MDD by examining smartphone-based objective data with deep learning approaches.
Collapse
Affiliation(s)
- Jae Sung Kim
- Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Bohyun Wang
- Department of Computer Science, Gachon University, Seongnam, Republic of Korea
| | - Meelim Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States
| | - Jung Lee
- Integrative Care Hub, Children's Hospital, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyungjun Kim
- AI.ble Therapeutics Inc, Seoul, Republic of Korea
| | - Danyeul Roh
- AI.ble Therapeutics Inc, Seoul, Republic of Korea
| | - Kyung Hwa Lee
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Soon-Beom Hong
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Joon Shik Lim
- Department of Computer Science, Gachon University, Seongnam, Republic of Korea
| | - Jae-Won Kim
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Neal Ryan
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| |
Collapse
|
7
|
Sheu YH, Magdamo C, Miller M, Das S, Blacker D, Smoller JW. AI-assisted prediction of differential response to antidepressant classes using electronic health records. NPJ Digit Med 2023; 6:73. [PMID: 37100858 PMCID: PMC10133261 DOI: 10.1038/s41746-023-00817-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 04/04/2023] [Indexed: 04/28/2023] Open
Abstract
Antidepressant selection is largely a trial-and-error process. We used electronic health record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants classes (SSRI, SNRI, bupropion, and mirtazapine) 4 to 12 weeks after antidepressant initiation. The final data set comprised 17,556 patients. Predictors were derived from both structured and unstructured EHR data and models accounted for features predictive of treatment selection to minimize confounding by indication. Outcome labels were derived through expert chart review and AI-automated imputation. Regularized generalized linear model (GLM), random forest, gradient boosting machine (GBM), and deep neural network (DNN) models were trained and their performance compared. Predictor importance scores were derived using SHapley Additive exPlanations (SHAP). All models demonstrated similarly good prediction performance (AUROCs ≥ 0.70, AUPRCs ≥ 0.68). The models can estimate differential treatment response probabilities both between patients and between antidepressant classes for the same patient. In addition, patient-specific factors driving response probabilities for each antidepressant class can be generated. We show that antidepressant response can be accurately predicted from real-world EHR data with AI modeling, and our approach could inform further development of clinical decision support systems for more effective treatment selection.
Collapse
Affiliation(s)
- Yi-Han Sheu
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Matthew Miller
- Harvard Injury Control Research Center, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Bouvé College of Health Sciences, Northeastern University, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
| | - Deborah Blacker
- Department of Psychiatry, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jordan W Smoller
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| |
Collapse
|
8
|
Squires M, Tao X, Elangovan S, Gururajan R, Zhou X, Acharya UR, Li Y. Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment. Brain Inform 2023; 10:10. [PMID: 37093301 PMCID: PMC10123592 DOI: 10.1186/s40708-023-00188-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/08/2023] [Indexed: 04/25/2023] Open
Abstract
Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis, and treatment of depression. In particular, precision psychiatry is an emerging field that utilises advanced computational techniques to achieve a more individualised approach to mental health care. This survey provides an overview of the ways in which artificial intelligence is currently being used to support precision psychiatry. Advanced algorithms are being used to support all phases of the treatment cycle. These systems have the potential to identify individuals suffering from mental health conditions, allowing them to receive the care they need and tailor treatments to individual patients who are mostly to benefit. Additionally, unsupervised learning techniques are breaking down existing discrete diagnostic categories and highlighting the vast disease heterogeneity observed within depression diagnoses. Artificial intelligence also provides the opportunity to shift towards evidence-based treatment prescription, moving away from existing methods based on group averages. However, our analysis suggests there are several limitations currently inhibiting the progress of data-driven paradigms in care. Significantly, none of the surveyed articles demonstrate empirically improved patient outcomes over existing methods. Furthermore, greater consideration needs to be given to uncertainty quantification, model validation, constructing interdisciplinary teams of researchers, improved access to diverse data and standardised definitions within the field. Empirical validation of computer algorithms via randomised control trials which demonstrate measurable improvement to patient outcomes are the next step in progressing models to clinical implementation.
Collapse
Affiliation(s)
- Matthew Squires
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia.
| | - Xiaohui Tao
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia
| | | | - Raj Gururajan
- School of Business, University of Southern Queensland, Springfield, QLD, Australia
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, Springfield, QLD, Australia
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia
| | - Yuefeng Li
- School of Computer Science, Queensland University of Technology, Brisbane, QLD, Australia
| |
Collapse
|
9
|
Scala JJ, Ganz AB, Snyder MP. Precision Medicine Approaches to Mental Health Care. Physiology (Bethesda) 2023; 38:0. [PMID: 36099270 PMCID: PMC9870582 DOI: 10.1152/physiol.00013.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 08/08/2022] [Accepted: 09/12/2022] [Indexed: 02/04/2023] Open
Abstract
Developing a more comprehensive understanding of the physiological underpinnings of mental illness, precision medicine has the potential to revolutionize psychiatric care. With recent breakthroughs in next-generation multi-omics technologies and data analytics, it is becoming more feasible to leverage multimodal biomarkers, from genetic variants to neuroimaging biomarkers, to objectify diagnostics and treatment decisions in psychiatry and improve patient outcomes. Ongoing work in precision psychiatry will parallel progress in precision oncology and cardiology to develop an expanded suite of blood- and neuroimaging-based diagnostic tests, empower monitoring of treatment efficacy over time, and reduce patient exposure to ineffective treatments. The emerging model of precision psychiatry has the potential to mitigate some of psychiatry's most pressing issues, including improving disease classification, lengthy treatment duration, and suboptimal treatment outcomes. This narrative-style review summarizes some of the emerging breakthroughs and recurring challenges in the application of precision medicine approaches to mental health care.
Collapse
Affiliation(s)
- Jack J Scala
- Department of Genetics, Stanford University, Stanford, California
| | - Ariel B Ganz
- Department of Genetics, Stanford University, Stanford, California
| | - Michael P Snyder
- Department of Genetics, Stanford University, Stanford, California
| |
Collapse
|
10
|
Fuh SC, Fiori LM, Turecki G, Nagy C, Li Y. Multi-omic modeling of antidepressant response implicates dynamic immune and inflammatory changes in individuals who respond to treatment. PLoS One 2023; 18:e0285123. [PMID: 37186582 PMCID: PMC10184917 DOI: 10.1371/journal.pone.0285123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/15/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Major depressive disorder (MDD) is a leading cause of disability worldwide, and is commonly treated with antidepressant drugs (AD). Although effective, many patients fail to respond to AD treatment, and accordingly identifying factors that can predict AD response would greatly improve treatment outcomes. In this study, we developed a machine learning tool to integrate multi-omic datasets (gene expression, DNA methylation, and genotyping) to identify biomarker profiles associated with AD response in a cohort of individuals with MDD. MATERIALS AND METHODS Individuals with MDD (N = 111) were treated for 8 weeks with antidepressants and were separated into responders and non-responders based on the Montgomery-Åsberg Depression Rating Scale (MADRS). Using peripheral blood samples, we performed RNA-sequencing, assessed DNA methylation using the Illumina EPIC array, and performed genotyping using the Illumina PsychArray. To address this rich multi-omic dataset with high dimensional features, we developed integrative Geneset-Embedded non-negative Matrix factorization (iGEM), a non-negative matrix factorization (NMF) based model, supplemented with auxiliary information regarding gene sets and gene-methylation relationships. In particular, we factorize the subjects by features (i.e., gene expression or DNA methylation) into subjects-by-factors and factors-by-features. We define the factors as the meta-phenotypes as they represent integrated composite scores of the molecular measurements for each subject. RESULTS Using our model, we identified a number of meta-phenotypes which were related to AD response. By integrating geneset information into the model, we were able to relate these meta-phenotypes to biological processes, including a meta-phenotype related to immune and inflammatory functions as well as other genes related to depression or AD response. The meta-phenotype identified several genes including immune interleukin 1 receptor like 1 (IL1RL1) and interleukin 5 receptor (IL5) subunit alpha (IL5RA), AKT/PIK3 pathway related phosphoinositide-3-kinase regulatory subunit 6 (PIK3R6), and sphingomyelin phosphodiesterase 3 (SMPD3), which has been identified as a target of AD treatment. CONCLUSIONS The derived meta-phenotypes and associated biological functions represent both biomarkers to predict response, as well as potential new treatment targets. Our method is applicable to other diseases with multi-omic data, and the software is open source and available on Github (https://github.com/li-lab-mcgill/iGEM).
Collapse
Affiliation(s)
- Shih-Chieh Fuh
- School of Computer Science, McGill University, Rue University, Montréal, Quebec, Canada
| | - Laura M Fiori
- Department of Psychiatry, McGill Group for Suicide Studies, Douglas Mental Health University, Montreal, Quebec, Canada
| | - Gustavo Turecki
- Department of Psychiatry, McGill Group for Suicide Studies, Douglas Mental Health University, Montreal, Quebec, Canada
| | - Corina Nagy
- Department of Psychiatry, McGill Group for Suicide Studies, Douglas Mental Health University, Montreal, Quebec, Canada
| | - Yue Li
- School of Computer Science, McGill University, Rue University, Montréal, Quebec, Canada
| |
Collapse
|
11
|
Ma H, Zhang D, Sun D, Wang H, Yang J. Gray and white matter structural examination for diagnosis of major depressive disorder and subthreshold depression in adolescents and young adults: a preliminary radiomics analysis. BMC Med Imaging 2022; 22:164. [PMID: 36096776 PMCID: PMC9465920 DOI: 10.1186/s12880-022-00892-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 09/06/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Radiomics is an emerging image analysis framework that provides more details than conventional methods. In present study, we aimed to identify structural radiomics features of gray matter (GM) and white matter (WM), and to develop and validate the classification model for major depressive disorder (MDD) and subthreshold depression (StD) diagnosis using radiomics analysis. METHODS A consecutive cohort of 142 adolescents and young adults, including 43 cases with MDD, 49 cases with StD and 50 healthy controls (HC), were recruited and underwent the three-dimensional T1 weighted imaging (3D-T1WI) and diffusion tensor imaging (DTI). We extracted radiomics features representing the shape and diffusion properties of GM and WM from all participants. Then, an all-relevant feature selection process embedded in a 10-fold cross-validation framework was used to identify features with significant power for discrimination. Random forest classifiers (RFC) were established and evaluated successively using identified features. RESULTS The results showed that a total of 3030 features were extracted after preprocessing, including 2262 shape-related features from each T1-weighted image representing GM morphometry and 768 features from each DTI representing the diffusion properties of WM. 25 features were selected ultimately, including ten features for MDD versus HC, eight features for StD versus HC, and seven features for MDD versus StD. The accuracies and area under curve (AUC) the RFC achieved were 86.75%, 0.93 for distinguishing MDD from HC with significant radiomics features located in the left medial orbitofrontal cortex, right superior and middle temporal regions, right anterior cingulate, left cuneus and hippocampus, 70.51%, 0.69 for discriminating StD from HC within left cuneus, medial orbitofrontal cortex, cerebellar vermis, hippocampus, anterior cingulate and amygdala, right superior and middle temporal regions, and 59.15%, 0.66 for differentiating MDD from StD within left medial orbitofrontal cortex, middle temporal and cuneus, right superior frontal, superior temporal regions and hippocampus, anterior cingulate, respectively. CONCLUSION These findings provide preliminary evidence that radiomics features of brain structure are valid for discriminating MDD and StD subjects from healthy controls. The MRI-based radiomics approach, with further improvement and validation, might be a potential facilitating method to clinical diagnosis of MDD or StD.
Collapse
Affiliation(s)
- Huan Ma
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, 374# DianMian Road, 650101, Kunming, China
| | - Dafu Zhang
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, 650018, Kunming, China
| | - Dewei Sun
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
| | - Hongbo Wang
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650018, China
| | - Jianzhong Yang
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, 374# DianMian Road, 650101, Kunming, China.
| |
Collapse
|
12
|
Lin E, Lin CH, Lane HY. Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2021; 19:577-588. [PMID: 34690113 PMCID: PMC8553527 DOI: 10.9758/cpn.2021.19.4.577] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/10/2021] [Indexed: 12/31/2022]
Abstract
A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.
Collapse
Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
- School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan
- Department of Brain Disease Research Center, China Medical University Hospital, Taichung, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan
| |
Collapse
|
13
|
Rema J, Novais F, Telles-Correia D. Precision Psychiatry: Machine learning as a tool to find new pharmacological targets. Curr Top Med Chem 2021; 22:1261-1269. [PMID: 34607546 DOI: 10.2174/1568026621666211004095917] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 07/20/2021] [Accepted: 08/19/2021] [Indexed: 12/18/2022]
Abstract
There is an increasing amount of data arising from neurobehavioral sciences and medical records that cannot be adequately analyzed by traditional research methods. New drugs develop at a slow rate and seem unsatisfactory for the majority of neurobehavioral disorders. Machine learning (ML) techniques, instead, can incorporate psychopathological, computational, cognitive, and neurobiological underpinning knowledge leading to a refinement of detection, diagnosis, prognosis, treatment, research, and support. Machine and deep learning methods are currently used to accelerate the process of discovering new pharmacological targets and drugs. OBJECTIVE The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets. METHODS Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review. RESULTS The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents. CONCLUSION Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.
Collapse
Affiliation(s)
- João Rema
- Faculdade de Medicina da Universidade de Lisboa. Portugal
| | - Filipa Novais
- Faculdade de Medicina da Universidade de Lisboa. Portugal
| | | |
Collapse
|
14
|
Drug–target interaction prediction using artificial intelligence. APPLIED NANOSCIENCE 2021. [DOI: 10.1007/s13204-021-02000-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
15
|
Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
Collapse
Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| |
Collapse
|
16
|
De Berardis D, Tomasetti C, Pompili M, Serafini G, Vellante F, Fornaro M, Valchera A, Perna G, Volpe U, Martinotti G, Fraticelli S, Di Giannantonio M, Kim YK, Orsolini L. An Update on Glutamatergic System in Suicidal Depression and on the Role of Esketamine. Curr Top Med Chem 2021; 20:554-584. [PMID: 32003691 DOI: 10.2174/1568026620666200131100316] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 11/15/2019] [Accepted: 12/09/2019] [Indexed: 12/22/2022]
Abstract
BACKGROUND A research on mood disorder pathophysiology has hypothesized abnormalities in glutamatergic neurotransmission, by suggesting further investigation on glutamatergic N-methyl-Daspartate (NMDA) receptor modulators in treating Major Depressive Disorder (MDD). Esketamine (ESK), an NMDA receptor antagonist able to modulate glutamatergic neurotransmission has been recently developed as an intranasal formulation for treatment-resistant depression (TRD) and for rapid reduction of depressive symptomatology, including suicidal ideation in MDD patients at imminent risk for suicide. OBJECTIVE The present study aims at investigating recent clinical findings on research on the role of the glutamatergic system and ESK in treating suicidal depression in MDD and TRD. METHODS A systematic review was here carried out on PubMed/Medline, Scopus and the database on U.S. N.I.H. Clinical Trials (https://clinicaltrials.gov) and the European Medical Agency (EMA) (https://clinicaltrialsregister.eu) from inception until October 2019. RESULTS Intravenous infusion of ESK is reported to elicit rapid-acting and sustained antidepressant activity in refractory patients with MDD and TRD. In phase II studies, intranasal ESK demonstrated a rapid onset and a persistent efficacy in patients with TRD as well as in MDD patients at imminent risk for suicide. However, some data discrepancies have emerged in phase III studies. CONCLUSION The U.S. Food and Drug Administration (FDA) granted fast track and Breakthrough Therapy Designation to Janssen Pharmaceuticals®, Inc. for intranasal ESK in 2013 for treatment-resistant depression (TRD) and in 2016 for the treatment of MDD with an imminent risk of suicide. However, further studies should be implemented to investigate the long-term efficacy and safety of intranasal ESK.
Collapse
Affiliation(s)
- Domenico De Berardis
- Department of Neuroscience, Imaging and Clinical Science, University of "G. D'Annunzio", Chieti, Italy.,National Health Service, Department of Mental Health, Psychiatric Service of Diagnosis and Treatment, Hospital "G. Mazzini", ASL 4 Teramo, Italy.,Polyedra, Teramo, Italy
| | - Carmine Tomasetti
- Polyedra, Teramo, Italy.,Department of Psychiatry, Federico II University, Naples, Italy.,NHS, Department of Mental Health, Psychiatric Service of Diagnosis and Treatment, Hospital "SS. Annunziata", ASL 4 Giulianova, Italy
| | - Maurizio Pompili
- Department of Neurosciences, Mental Health and Sensory Organs, Suicide Prevention Center, S. Andrea Hospital, Sapienza University, Rome, Italy
| | - Gianluca Serafini
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Section of Psychiatry, University of Genoa, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Federica Vellante
- Department of Neuroscience, Imaging and Clinical Science, University of "G. D'Annunzio", Chieti, Italy
| | - Michele Fornaro
- Polyedra, Teramo, Italy.,Department of Psychiatry, Federico II University, Naples, Italy
| | - Alessandro Valchera
- Polyedra, Teramo, Italy.,Villa S. Giuseppe Hospital, Hermanas Hospitalarias, Ascoli Piceno, Italy
| | - Giampaolo Perna
- Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Como, Italy.,Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.,Department of Psychiatry and Behavioral Sciences, Leonard Miller School of Medicine, Miami University, Miami 786, United States
| | - Umberto Volpe
- Department of Clinical Neurosciences/DIMSC, School of Medicine, Section of Psychiatry, Polytechnic University of Marche, Ancona, Italy
| | - Giovanni Martinotti
- Department of Neuroscience, Imaging and Clinical Science, University of "G. D'Annunzio", Chieti, Italy
| | - Silvia Fraticelli
- Department of Neuroscience, Imaging and Clinical Science, University of "G. D'Annunzio", Chieti, Italy
| | - Massimo Di Giannantonio
- Department of Neuroscience, Imaging and Clinical Science, University of "G. D'Annunzio", Chieti, Italy
| | - Yong-Ku Kim
- Department of Psychiatry, Korea University College of Medicine, Seoul, Korea
| | - Laura Orsolini
- Polyedra, Teramo, Italy.,Department of Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield, Herts, United Kingdom.,Neomesia Mental Health, Villa Jolanda Hospital, Maiolati Spontini, Italy
| |
Collapse
|
17
|
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: 32] [Impact Index Per Article: 8.0] [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.
Collapse
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.)
| |
Collapse
|
18
|
Li C, Zheng Y, Huang P. Molecular markers from the chloroplast genome of rose provide a complementary tool for variety discrimination and profiling. Sci Rep 2020; 10:12188. [PMID: 32699274 PMCID: PMC7376030 DOI: 10.1038/s41598-020-68092-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 06/17/2020] [Indexed: 02/06/2023] Open
Abstract
The rose is one of the most important ornamental woody plants because of its extensive use and high economic value. Herein, we sequenced a complete chloroplast genome of the miniature rose variety Rosa 'Margo Koster' and performed comparative analyses with sequences previously published for other species in the Rosaceae family. The chloroplast genome of Rosa 'Margo Koster', with a size of 157,395 bp, has a circular quadripartite structure typical of angiosperm chloroplast genomes and contains a total of 81 protein-coding genes, 30 tRNA genes and 4 rRNA genes. Conjunction regions in the chloroplast genome of Rosa 'Margo Koster' were verified and manually corrected by Sanger sequencing. Comparative genome analysis showed that the IR contraction and expansion events resulted in rps19 and ycf1 pseudogenes. The phylogenetic analysis within the Rosa genus showed that Rosa 'Margo Koster' is closer to Rosa odorata than to other Rosa species. Additionally, we identified and screened highly divergent sequences and cpSSRs and compared their power to discriminate rose varieties by Sanger sequencing and capillary electrophoresis. The results showed that 15 cpSSRs are polymorphic, but their discriminating power is only moderate among a set of rose varieties. However, more than 150 single nucleotide variations (SNVs) were discovered in the flanking region of cpSSRs, and the results indicated that these SNVs have a higher divergence and stronger power for profiling rose varieties. These findings suggest that nucleotide mutations in the chloroplast genome may be an effective and powerful tool for rose variety discrimination and DNA profiling. These molecular markers in the chloroplast genome sequence of Rosa spp. will facilitate population and phylogenetic studies and other related studies of this species.
Collapse
Affiliation(s)
- Changhong Li
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Forest Silviculture and Tree Cultivation, State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
| | - Yongqi Zheng
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Forest Silviculture and Tree Cultivation, State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China.
| | - Ping Huang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Forest Silviculture and Tree Cultivation, State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China.
| |
Collapse
|
19
|
Manchia M, Pisanu C, Squassina A, Carpiniello B. Challenges and Future Prospects of Precision Medicine in Psychiatry. PHARMACOGENOMICS & PERSONALIZED MEDICINE 2020; 13:127-140. [PMID: 32425581 PMCID: PMC7186890 DOI: 10.2147/pgpm.s198225] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 04/14/2020] [Indexed: 12/21/2022]
Abstract
Precision medicine is increasingly recognized as a promising approach to improve disease treatment, taking into consideration the individual clinical and biological characteristics shared by specific subgroups of patients. In specific fields such as oncology and hematology, precision medicine has already started to be implemented in the clinical setting and molecular testing is routinely used to select treatments with higher efficacy and reduced adverse effects. The application of precision medicine in psychiatry is still in its early phases. However, there are already examples of predictive models based on clinical data or combinations of clinical, neuroimaging and biological data. While the power of single clinical predictors would remain inadequate if analyzed only with traditional statistical approaches, these predictors are now increasingly used to impute machine learning models that can have adequate accuracy even in the presence of relatively small sample size. These models have started to be applied to disentangle relevant clinical questions that could lead to a more effective management of psychiatric disorders, such as prediction of response to the mood stabilizer lithium, resistance to antidepressants in major depressive disorder or stratification of the risk and outcome prediction in schizophrenia. In this narrative review, we summarized the most important findings in precision medicine in psychiatry based on studies that constructed machine learning models using clinical, neuroimaging and/or biological data. Limitations and barriers to the implementation of precision psychiatry in the clinical setting, as well as possible solutions and future perspectives, will be presented.
Collapse
Affiliation(s)
- 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
| | - Claudia Pisanu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - Alessio Squassina
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy.,Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - 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
| |
Collapse
|
20
|
Lin E, Lin CH, Lane HY. Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches. Int J Mol Sci 2020; 21:ijms21030969. [PMID: 32024055 PMCID: PMC7037937 DOI: 10.3390/ijms21030969] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 01/25/2020] [Accepted: 01/30/2020] [Indexed: 12/22/2022] Open
Abstract
A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.
Collapse
Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA;
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
- Correspondence: (C.-H.L.); (H.-Y.L.)
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung 40402, Taiwan
- Brain Disease Research Center, China Medical University Hospital, Taichung 40402, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung 41354, Taiwan
- Correspondence: (C.-H.L.); (H.-Y.L.)
| |
Collapse
|