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Larsen SV, Ozenne B, Köhler-Forsberg K, Poulsen AS, Dam VH, Svarer C, Knudsen GM, Jørgensen MB, Frokjaer VG. The Impact of Hormonal Contraceptive Use on Serotonergic Neurotransmission and Antidepressant Treatment Response: Results From the NeuroPharm 1 Study. Front Endocrinol (Lausanne) 2022; 13:799675. [PMID: 35360055 PMCID: PMC8962375 DOI: 10.3389/fendo.2022.799675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/31/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Hormonal contraceptive (HC) use has been associated with an increased risk of developing a depressive episode. This might be related to HC's effect on the serotonergic brain system as suggested by recent cross-sectional data from our group, which show that healthy oral contraceptive (OC) users relative to non-users have lower cerebral serotonin 4 receptor (5-HT4R) levels. Here, we determine if cerebral 5-HT4R binding differs between HC non-users, OC users, and hormonal intrauterine device (HIUD) users among women with an untreated depressive episode. Also, we test if antidepressant drug treatment response and its association with pre-treatment 5-HT4R binding depends on HC status. METHODS [11C]-SB207145 Positron Emission Tomography imaging data from the NeuroPharm-NP1 Study (NCT02869035) were available from 59 depressed premenopausal women, of which 26 used OCs and 10 used HIUDs. The participants were treated with escitalopram. Treatment response was measured as the relative change in the Hamilton Depression Rating Scale 6 items (rΔHAMD6) from baseline to week eight. Latent variable models were used to evaluate the association between global 5-HT4R binding and OC and HIUD use as well as rΔHAMD6. RESULTS We found no evidence of a difference in global 5-HT4R binding between depressed HC users and non-users (p≥0.51). A significant crossover interaction (p=0.02) was observed between non-users and OC users in the association between baseline global 5-HT4R binding and week eight rΔHAMD6; OC users had 3-4% lower binding compared to non-users for every 10% percent less improvement in HAMD6. Within the groups, we observed a trend towards a positive association in non-users (padj=0.10) and a negative association in OC users (padj=0.07). We found no strong evidence of a difference in treatment response between the groups (p=0.13). CONCLUSIONS We found no difference in 5-HT4R binding between HC users vs. non-users in depressed women, however, it seemed that 5-HT4R settings differed qualitatively in their relation to antidepressant drug treatment response between OC users and non-users. From this we speculate that depressed OC users constitutes a special serotonin subtype of depression, which might have implications for antidepressant drug treatment response.
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Affiliation(s)
- Søren Vinther Larsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Kristin Köhler-Forsberg
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Psychiatric Center Copenhagen, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
| | | | - Vibeke Høyrup Dam
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Claus Svarer
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Gitte Moos Knudsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Martin Balslev Jørgensen
- Psychiatric Center Copenhagen, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
| | - Vibe Gedso Frokjaer
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Psychiatric Center Copenhagen, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
- *Correspondence: Vibe Gedso Frokjaer,
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Dai Z, Pei C, Zhang S, Tian S, Chen Z, Zhou H, Lu Q, Yao Z. Attenuated alpha-gamma coupling in emotional dual pathways with right-Amygdala predicting ineffective antidepressant response. CNS Neurosci Ther 2021; 28:401-410. [PMID: 34953030 PMCID: PMC8841302 DOI: 10.1111/cns.13787] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 11/10/2021] [Accepted: 12/03/2021] [Indexed: 11/27/2022] Open
Abstract
Aims The diversity of treatment outcomes for major depressive disorder (MDD) remains uncertain in neuropathology. The current study aimed at exploring electrophysiological biomarkers associated with treatment response. Methods The present study recruited 130 subjects including 100 MDD patients and 30 healthy controls. All subjects participated in a sad expression recognition task while their magnetoencephalography data were recorded. Patients who had a reduction of at least 50% in disorder severity at endpoint (>2 weeks) were considered as responders. Within‐frequency power and phase‐amplitude coupling were measured for the brain regions involved in the emotional visual information processing pathways. Results The significant alpha–gamma decoupling from the right thalamus to the right amygdala in unconscious processing and from right orbital frontal cortices to the right amygdala in conscious processing was found in non‐responders relative to responders and healthy controls. These kinds of dysregulation could also predict the potential treatment response. Conclusion The attenuated alpha–gamma coupling in dual pathways indicated increased sensitivity to the negative emotional information and reduced moderated effect of the amygdala, which might cause insensitivity to antidepressant treatment and could be regarded as potential neural mechanisms for treatment response prediction.
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Affiliation(s)
- Zhongpeng Dai
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Research Center for Learning Science, Ministry of Education, Southeast University, Nanjing, China
| | - Cong Pei
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Research Center for Learning Science, Ministry of Education, Southeast University, Nanjing, China
| | - Siqi Zhang
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Research Center for Learning Science, Ministry of Education, Southeast University, Nanjing, China
| | - Shui Tian
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Research Center for Learning Science, Ministry of Education, Southeast University, Nanjing, China
| | - Zhilu Chen
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Hongliang Zhou
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Qing Lu
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Research Center for Learning Science, Ministry of Education, Southeast University, Nanjing, China
| | - Zhijian Yao
- Key Laboratory of Child Development and Learning Science, School of Biological Sciences & Medical Engineering, Child Development and Learning Science, Research Center for Learning Science, Ministry of Education, Southeast University, Nanjing, China.,Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
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53
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Wu CS, Yang AC, Chang SS, Chang CM, Liu YH, Liao SC, Tsai HJ. Validation of Machine Learning-Based Individualized Treatment for Depressive Disorder Using Target Trial Emulation. J Pers Med 2021; 11:jpm11121316. [PMID: 34945788 PMCID: PMC8706481 DOI: 10.3390/jpm11121316] [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] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 11/30/2021] [Accepted: 12/01/2021] [Indexed: 12/20/2022] Open
Abstract
This study aims to develop and validate the use of machine learning-based prediction models to select individualized pharmacological treatment for patients with depressive disorder. This study used data from Taiwan’s National Health Insurance Research Database. Patients with incident depressive disorders were included in this study. The study outcome was treatment failure, which was defined as psychiatric hospitalization, self-harm hospitalization, emergency visits, or treatment change. Prediction models based on the Super Learner ensemble were trained separately for the initial and the next-step treatments if the previous treatments failed. An individualized treatment strategy was developed for selecting the drug with the lowest probability of treatment failure for each patient as the model-selected regimen. We emulated clinical trials to estimate the effectiveness of individualized treatments. The area under the curve of the prediction model using Super Learner was 0.627 and 0.751 for the initial treatment and the next-step treatment, respectively. Model-selected regimens were associated with reduced treatment failure rates, with a 0.84-fold (95% confidence interval (CI) 0.82–0.86) decrease for the initial treatment and a 0.82-fold (95% CI 0.80–0.83) decrease for the next-step. In emulation of clinical trials, the model-selected regimen was associated with a reduced treatment failure rate.
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Affiliation(s)
- Chi-Shin Wu
- National Centre for Geriatrics and Welfare Research, National Health Research Institutes, Zhunan 350, Taiwan
- Department of Psychiatry, Yunlin Branch, National Taiwan University Hospital, Yunlin 632, Taiwan
- Correspondence:
| | - Albert C. Yang
- Digital Medicine Center, Institute of Brain Science, National Yang-Ming Chiao-Tung University, Taipei 112, Taiwan;
| | - Shu-Sen Chang
- Institute of Health Behaviours and Community Sciences, College of Public Health, National Taiwan University, Taipei 112, Taiwan;
| | - Chia-Ming Chang
- Department of Psychiatry, Chang Gung Memorial Hospital, Linkou and Chang Gung University, Taoyuan 333, Taiwan;
| | - Yi-Hung Liu
- Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan;
| | - Shih-Cheng Liao
- Department of Psychiatry, College of Medicine, National Taiwan University Hospital, National Taiwan University, Taipei 100, Taiwan;
| | - Hui-Ju Tsai
- Institute of Population Health Sciences, National Health Research Institutes, Zhunan 350, Taiwan;
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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.
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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
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Huang J, Yu Y, Jiang Y, Chen W, Li Y, Shen Y, Zheng Q, Li H. The efficacy and safety of Anyu Peibo Capsule in the treatment of patients with major depressive disorder in China: study protocol for a randomized placebo-controlled trial. Trials 2021; 22:585. [PMID: 34479619 PMCID: PMC8414707 DOI: 10.1186/s13063-021-05550-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 08/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Major depressive disorder is the second leading cause of years lost to disability worldwide. Anyu Peibo Capsule has been shown to be effective and safe in phase II trials. METHODS This clinical study is a multi-center, randomized, double-blinded, placebo-controlled, parallel-group, phase III trial of Anyu Peibo Capsule in China. The aim is to test whether the administration of Anyu Peibo Capsule compared to placebo improves clinical outcomes in adults (aged 18 to 65 years) with MDD. Patients will receive an 8-week treatment of Anyu Peibo Capsule 1.6 g per day or placebo. The primary outcome will be the change from baseline in the total score for the Montgomery-Asberg Depression Rating Scale at the end of the 8-week treatment. DISCUSSION The trial aims to provide pivotal evidence for the efficacy and safety of Anyu Peibo Capsule in patients with major depressive disorder. TRIAL REGISTRATION ClinicalTrials.gov NCT04210973 . Registered on December 26, 2019.
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Affiliation(s)
- Jingjing Huang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030, People's Republic of China
| | - Yimin Yu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030, People's Republic of China
| | - Yi Jiang
- Su Zhou YiHua Biotechnology Co. Ltd., Suzhou, People's Republic of China
| | - Wu Chen
- Su Zhou YiHua Biotechnology Co. Ltd., Suzhou, People's Republic of China
| | - Yan Li
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030, People's Republic of China
| | - Yifeng Shen
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030, People's Republic of China
| | - Qingshan Zheng
- Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
| | - Huafang Li
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030, People's Republic of China.
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González-Roz A, Secades-Villa R, García-Fernández G, Martínez-Loredo V, Alonso-Pérez F. Depression symptom profiles and long-term response to cognitive behavioral therapy plus contingency management for smoking cessation. Drug Alcohol Depend 2021; 225:108808. [PMID: 34198211 DOI: 10.1016/j.drugalcdep.2021.108808] [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: 01/07/2021] [Revised: 04/22/2021] [Accepted: 04/25/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Depression is heterogeneous in nature and using diagnostic categories limits insight into understanding psychopathology and its impact on treatment efficacy. This secondary analysis sought to: 1) identify distinct subpopulations of cigarette users with depression, and 2) examine their response to cognitive-behavioral treatment (CBT) + contingency management (CM) for smoking cessation at one year. METHOD The sample comprised 238 (74 % females) adults who smoke receiving CBT only or CBT + CM. A latent class analysis was conducted on baseline depressive symptoms measured using the Beck Depression Inventory-II. Generalized estimating equations assessed the main and interactive effects of class, time, treatment, and sex on smoking abstinence. RESULTS Three distinct classes were identified: C1 (n= 76/238), characterized by mild depression, loss of energy, pessimism, and criticism, C2 (n= 100/238) presenting moderate severity and decreased appetite, and C3 (n= 62/238) showing severe depression, increased appetite, and feelings of punishment. There was a significant cluster × treatment interaction, which indicated additive effects of CM over CBT alone for Class 1 and 2. Persons in Class 1 and 2 were 3.60 [95 % CI: 1.62, 7.97] and 2.65 [95 % CI: 1.19, 5.91] times more likely to be abstinent if CBT + CM was delivered rather than CBT only. No differential sex effects were observed on treatment response according to cluster. CONCLUSIONS Profiling depression symptom subtypes of cigarette users may be more informative to improve CM treatment response than merely focusing on total scores.
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Affiliation(s)
- Alba González-Roz
- Department of Psychology/Research Institute of Health Sciences (IUNICS), University of the Balearic Islands, Spain; Department of Psychology, University of Oviedo, Spain.
| | | | | | - Víctor Martínez-Loredo
- Department of Psychology, University of Oviedo, Spain; Department of Psychology and Sociology, University of Zaragoza, Spain
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57
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Zazula R, Husain MI, Mohebbi M, Walker AJ, Chaudhry IB, Khoso AB, Ashton MM, Agustini B, Husain N, Deakin J, Young AH, Berk M, Kanchanatawan B, Ng CH, Maes M, Berk L, Singh AB, Malhi GS, Dean OM. Minocycline as adjunctive treatment for major depressive disorder: Pooled data from two randomized controlled trials. Aust N Z J Psychiatry 2021; 55:784-798. [PMID: 33092404 DOI: 10.1177/0004867420965697] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Randomized controlled clinical trials that have investigated minocycline as an adjunctive treatment for major depressive disorder have proved promising. Data from two studies were pooled to evaluate more definitively whether the addition of minocycline to standard treatment for major depressive disorder leads to an improvement of depressive symptoms when compared with placebo. METHODS Both studies were multi-site, double-blinded, placebo-controlled trials of minocycline 200 mg/day added to treatment as usual during a 12-week period. The primary outcome measure was change in depressive symptoms (Montgomery-Asberg Depression Rating Scale in Dean et al. and Hamilton Depression Rating Scale in Husain et al.). Secondary outcomes were change in depression severity (Montgomery-Asberg Depression Rating Scale for Dean et al. and 9-item Patient Health Questionnaire in Husain et al.), anxiety severity (Hamilton Anxiety Rating Scale in Dean et al. and Generalized Anxiety Disorder 7-item scale in Husain et al.) and functional status, which were also evaluated as potential mediators on the primary outcome. RESULTS A total of 112 participants were included in the pooled data (Dean et al., n = 71; Husain et al., n = 41). A significant change from baseline to week 12 was noted in depressive symptoms - differential change (Placebo vs Minocycline): 9.0, 95% confidence interval = [4.2, 13.9], Cohen's D (95% confidence interval): 0.71 [0.29, 1.14], p < 0.001 - anxiety severity - differential change (Placebo vs Minocycline): 0.38, confidence interval = [0.00, 0.75], Cohen's D (95% confidence interval): 0.41 [0.00, 0.82], p = 0.050) and functional status - differential change (Placebo vs Minocycline): 1.0, 95% confidence interval = [0.4, 1.5], Cohen's D (95% confidence interval): 0.76 [0.34, 1.19], p = 0.001). Duration of illness, current use of benzodiazepine and pain medication were identified as moderators, whereas functional status as a mediator/predictor. CONCLUSION The improvement of depressive symptoms, anxiety severity and functional status is promising and suggests that minocycline has potential as an adjunctive treatment for major depressive disorder. However, further studies are warranted to confirm therapeutic effects of minocycline in major depressive disorder. TRIAL REGISTRATIONS NCT02263872, registered October 2014, and ACTRN12612000283875, registered March 2012.
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Affiliation(s)
- Robson Zazula
- Latin American Institute for the Science of Life and Nature, Federal University of Latin American Integration, Foz do Iguacu, Brazil.,Health Sciences Graduate Program, Londrina State University, Londrina, Brazil.,Deakin University, iMPACT, the Institute for Mental and Physical Health and Clinical Translation, Barwon Health, Geelong, Australia
| | - Muhammad Ishrat Husain
- Department of Psychiatry, University of Toronto, Toronto, Canada.,Centre for Addiction and Mental Health, Toronto, Canada
| | - Mohammadreza Mohebbi
- Deakin University, iMPACT, the Institute for Mental and Physical Health and Clinical Translation, Barwon Health, Geelong, Australia.,Deakin University, Faculty of Health, Biostatistics Unit, Geelong, Australia
| | - Adam J Walker
- Deakin University, iMPACT, the Institute for Mental and Physical Health and Clinical Translation, Barwon Health, Geelong, Australia
| | - Imran B Chaudhry
- Department of Psychiatry, Ziauddin University, Karachi, Pakistan.,Pakistan Institute of Living and Learning, Karachi, Pakistan.,University of Manchester, Manchester, UK
| | - Ameer B Khoso
- Pakistan Institute of Living and Learning, Karachi, Pakistan
| | - Melanie M Ashton
- Deakin University, iMPACT, the Institute for Mental and Physical Health and Clinical Translation, Barwon Health, Geelong, Australia
| | - Bruno Agustini
- Deakin University, iMPACT, the Institute for Mental and Physical Health and Clinical Translation, Barwon Health, Geelong, Australia
| | | | - Jfw Deakin
- University of Manchester, Manchester, UK
| | - Allan H Young
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.,South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Monks Orchard Road, Beckenham, Kent, UK
| | - Michael Berk
- Deakin University, iMPACT, the Institute for Mental and Physical Health and Clinical Translation, Barwon Health, Geelong, Australia.,Florey Institute for Neuroscience and Mental Health, University of Melbourne, Australia.,The Melbourne Clinic, Department of Psychiatry, University of Melbourne, Australia.,Orygen the National Centre of Excellence in Youth Mental Health, Parkville, Australia
| | | | - Chee H Ng
- The Melbourne Clinic, Department of Psychiatry, University of Melbourne, Australia
| | - Michael Maes
- Deakin University, iMPACT, the Institute for Mental and Physical Health and Clinical Translation, Barwon Health, Geelong, Australia.,Department of Psychiatry, Chulalongkorn University, Bangkok, Thailand
| | - Lesley Berk
- Deakin University, iMPACT, the Institute for Mental and Physical Health and Clinical Translation, Barwon Health, Geelong, Australia.,Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Ajeet B Singh
- Deakin University, iMPACT, the Institute for Mental and Physical Health and Clinical Translation, Barwon Health, Geelong, Australia
| | - Gin S Malhi
- The University of Sydney, Faculty of Medicine and Health, Northern Clinical School, Department of Psychiatry, Sydney, New South Wales, Australia.,Academic Department of Psychiatry, Royal North Shore Hospital, Northern Sydney Australia.,CADE Clinic, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, Australia
| | - Olivia M Dean
- Deakin University, iMPACT, the Institute for Mental and Physical Health and Clinical Translation, Barwon Health, Geelong, Australia.,Florey Institute for Neuroscience and Mental Health, University of Melbourne, Australia
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Abstract
BACKGROUND A rapid antidepressant effect of ketamine has repeatedly been documented in the literature, and identifying clinical features associated with a better response to this treatment is currently an essential question. Considering the relationship between rumination and depression and the need to identify potential predictors of response to ketamine, we analyzed the effect of a single injection of ketamine 0.5 mg/kg on rumination in treatment-resistant depressive (TRD) patients and explored whether baseline ruminative style and early improvements of rumination would predict a greater antidepressant effect of ketamine. METHODS Ten TRD outpatients who participated in a 4-week open study on the antidepressant effect of ketamine also completed the Ruminative Response Scale the day before, the day after, and a week after ketamine administration. RESULTS We found that in our patients, a single rapid 1-minute intravenous injection of ketamine 0.5 mg/kg was efficacious in reducing rumination, but neither severity of rumination at baseline nor early improvements of rumination after ketamine injection predicted antidepressant response. CONCLUSIONS Our preliminary data suggest that a single injection of ketamine 0.5 mg/kg can be efficacious in reducing rumination in TRD patients but rumination does not seem to be a useful clinical predictor of response to ketamine. Larger studies are necessary to confirm these results.
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Optimizing prediction of response to antidepressant medications using machine learning and integrated genetic, clinical, and demographic data. Transl Psychiatry 2021; 11:381. [PMID: 34238923 PMCID: PMC8266902 DOI: 10.1038/s41398-021-01488-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 05/13/2021] [Accepted: 06/16/2021] [Indexed: 02/07/2023] Open
Abstract
Major depressive disorder (MDD) is complex and multifactorial, posing a major challenge of tailoring the optimal medication for each patient. Current practice for MDD treatment mainly relies on trial and error, with an estimated 42-53% response rates for antidepressant use. Here, we sought to generate an accurate predictor of response to a panel of antidepressants and optimize treatment selection using a data-driven approach analyzing combinations of genetic, clinical, and demographic factors. We analyzed the response patterns of patients to three antidepressant medications in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, and employed state-of-the-art machine learning (ML) tools to generate a predictive algorithm. To validate our results, we assessed the algorithm's capacity to predict individualized antidepressant responses on a separate set of 530 patients in STAR*D, consisting of 271 patients in a validation set and 259 patients in the final test set. This assessment yielded an average balanced accuracy rate of 72.3% (SD 8.1) and 70.1% (SD 6.8) across the different medications in the validation and test set, respectively (p < 0.01 for all models). To further validate our design scheme, we obtained data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) of patients treated with citalopram, and applied the algorithm's citalopram model. This external validation yielded highly similar results for STAR*D and PGRN-AMPS test sets, with a balanced accuracy of 60.5% and 61.3%, respectively (both p's < 0.01). These findings support the feasibility of using ML algorithms applied to large datasets with genetic, clinical, and demographic features to improve accuracy in antidepressant prescription.
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Wainberg M, Kloiber S, Diniz B, McIntyre RS, Felsky D, Tripathy SJ. Clinical laboratory tests and five-year incidence of major depressive disorder: a prospective cohort study of 433,890 participants from the UK Biobank. Transl Psychiatry 2021; 11:380. [PMID: 34234104 PMCID: PMC8263616 DOI: 10.1038/s41398-021-01505-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 06/16/2021] [Accepted: 06/24/2021] [Indexed: 11/23/2022] Open
Abstract
Prevention of major depressive disorder (MDD) is a public health priority. Identifying biomarkers of underlying biological processes that contribute to MDD onset may help address this public health need. This prospective cohort study encompassed 383,131 white British participants from the UK Biobank with no prior history of MDD, with replication in 50,759 participants of other ancestries. Leveraging linked inpatient and primary care records, we computed adjusted odds ratios for 5-year MDD incidence among individuals with values below or above the 95% confidence interval (<2.5th or >97.5th percentile) on each of 57 laboratory measures. Sensitivity analyses were performed across multiple percentile thresholds and in comparison to established reference ranges. We found that indicators of liver dysfunction were associated with increased 5-year MDD incidence (even after correction for alcohol use and body mass index): elevated alanine aminotransferase (AOR = 1.35, 95% confidence interval [1.16, 1.58]), aspartate aminotransferase (AOR = 1.39 [1.19, 1.62]), and gamma glutamyltransferase (AOR = 1.52 [1.31, 1.76]) as well as low albumin (AOR = 1.28 [1.09, 1.50]). Similar observations were made with respect to endocrine dysregulation, specifically low insulin-like growth factor 1 (AOR = 1.34 [1.16, 1.55]), low testosterone among males (AOR = 1.60 [1.27, 2.00]), and elevated glycated hemoglobin (HbA1C; AOR = 1.23 [1.05, 1.43]). Markers of renal impairment (i.e. elevated cystatin C, phosphate, and urea) and indicators of anemia and macrocytosis (i.e. red blood cell enlargement) were also associated with MDD incidence. While some immune markers, like elevated white blood cell and neutrophil count, were associated with MDD (AOR = 1.23 [1.07, 1.42]), others, like elevated C-reactive protein, were not (AOR = 1.04 [0.89, 1.22]). The 30 significant associations validated as a group in the multi-ancestry replication cohort (Wilcoxon p = 0.0005), with a median AOR of 1.235. Importantly, all 30 significant associations with extreme laboratory test results were directionally consistent with an increased MDD risk. In sum, markers of liver and kidney dysfunction, growth hormone and testosterone deficiency, innate immunity, anemia, macrocytosis, and insulin resistance were associated with MDD incidence in a large community-based cohort. Our results support a contributory role of diverse biological processes to MDD onset.
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Affiliation(s)
- Michael Wainberg
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Stefan Kloiber
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Breno Diniz
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Roger S McIntyre
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, ON, Canada
| | - Daniel Felsky
- Centre for Addiction and Mental Health, Toronto, ON, Canada
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Shreejoy J Tripathy
- Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Department of Physiology, University of Toronto, Toronto, ON, Canada.
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Dini H, Sendi MSE, Sui J, Fu Z, Espinoza R, Narr KL, Qi S, Abbott CC, van Rooij SJH, Riva-Posse P, Bruni LE, Mayberg HS, Calhoun VD. Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder. Front Hum Neurosci 2021; 15:689488. [PMID: 34295231 PMCID: PMC8291148 DOI: 10.3389/fnhum.2021.689488] [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] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/31/2021] [Indexed: 12/28/2022] Open
Abstract
Background: Electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of depressive disorder (DEP) patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network (dFNC) estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states. Method: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 119 patients with depression or depressive disorder (DEP) (76 females), and 61 healthy (HC) participants (34 females), with an age mean of 52.25 (N = 180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59 ± 6.14 and 11.48 ± 9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called “occupancy rate” or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants. Results: The main findings include (1) depressive disorder (DEP) patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network (CCN) and default mode network (DMN) was relatively higher than other states (corrected p = 0.015), (2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes (R = 0.23 corrected p = 0.03). This means that those DEP patients who spent less time in this state showed more HDRS change, and (3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2 (corrected p = 0.03). Conclusion: Our finding suggests that dynamic functional network connectivity (dFNC) features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients.
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Affiliation(s)
- Hossein Dini
- Department of Architecture, Design and Media Technology, Aalborg University, Copenhagen, Denmark
| | - Mohammad S E Sendi
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, United States.,Department of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Randall Espinoza
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Katherine L Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Shile Qi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Christopher C Abbott
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, United States
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Luis Emilio Bruni
- Department of Architecture, Design and Media Technology, Aalborg University, Copenhagen, Denmark
| | - Helen S Mayberg
- Departments of Neurology, Neurosurgery, Psychiatry and Neuroscience, Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Vince D Calhoun
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, United States.,Department of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
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Coulombe J, Moodie EEM, Shortreed SM, Renoux C. Can the Risk of Severe Depression-Related Outcomes Be Reduced by Tailoring the Antidepressant Therapy to Patient Characteristics? Am J Epidemiol 2021; 190:1210-1219. [PMID: 33295950 DOI: 10.1093/aje/kwaa260] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 09/29/2020] [Accepted: 10/08/2020] [Indexed: 12/29/2022] Open
Abstract
Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for patients with unipolar depression, yet there is little guidance on which SSRI provides the most benefit to a patient, based on personal characteristics. In this work, we explore whether an individualized treatment strategy can be used by health-care providers to adapt their prescription pattern to reduce the risk of a severe depression-related outcome (SDO) when choosing between citalopram and fluoxetine, 2 commonly prescribed SSRIs. Our population-based cohort study used data from the Clinical Practice Research Datalink, the Hospital Episode Statistics repository, and the Office for National Statistics database in the United Kingdom to create a cohort of individuals diagnosed with depression who were prescribed citalopram or fluoxetine between April 1998 and December 2017. Patients were followed from treatment initiation until occurrence of the SDO outcome, treatment discontinuation, or end of study. To find an optimal treatment strategy, we used dynamic weighted survival modeling, considering patient features such as age, sex, body mass index, previous psychiatric diagnoses, and medications. Our findings suggest that using patient characteristics to tailor the antidepressant drug therapy is associated with an increase of 4 days in the median time to SDO (95% confidence interval: 2, 10 days).
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Prediction of drug response in major depressive disorder using ensemble of transfer learning with convolutional neural network based on EEG. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.06.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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The clinical effectiveness of using a predictive algorithm to guide antidepressant treatment in primary care (PReDicT): an open-label, randomised controlled trial. Neuropsychopharmacology 2021; 46:1307-1314. [PMID: 33637837 PMCID: PMC8134561 DOI: 10.1038/s41386-021-00981-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 01/25/2021] [Accepted: 01/27/2021] [Indexed: 11/25/2022]
Abstract
Depressed patients often do not respond to the first antidepressant prescribed, resulting in sequential trials of different medications. Personalised medicine offers a means of reducing this delay; however, the clinical effectiveness of personalised approaches to antidepressant treatment has not previously been tested. We assessed the clinical effectiveness of using a predictive algorithm, based on behavioural tests of affective cognition and subjective symptoms, to guide antidepressant treatment. We conducted a multicentre, open-label, randomised controlled trial in 913 medication-free depressed patients. Patients were randomly assigned to have their antidepressant treatment guided by a predictive algorithm or treatment as usual (TaU). The primary outcome was the response of depression symptoms, defined as a 50% or greater reduction in baseline score of the QIDS-SR-16 scale, at week 8. Additional prespecified outcomes included symptoms of anxiety at week 8, and symptoms of depression and functional outcome at weeks 8, 24 and 48. The response rate of depressive symptoms at week 8 in the PReDicT (55.9%) and TaU (51.8%) arms did not differ significantly (odds ratio: 1.18 (95% CI: 0.89-1.56), P = 0.25). However, there was a significantly greater reduction of anxiety in week 8 and a greater improvement in functional outcome at week 24 in the PReDicT arm. Use of the PReDicT test did not increase the rate of response to antidepressant treatment estimated by depressive symptoms but did improve symptoms of anxiety at week 8 and functional outcome at week 24. Our findings indicate that personalisation of antidepressant treatment may improve outcomes in depressed patients.
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Yoon S, Kim Y, Lee SH. Does the Loudness Dependence of Auditory Evoked Potential Predict Response to Selective Serotonin Reuptake Inhibitors?: A Meta-analysis. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE 2021; 19:254-261. [PMID: 33888654 PMCID: PMC8077049 DOI: 10.9758/cpn.2021.19.2.254] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 06/05/2020] [Accepted: 06/16/2020] [Indexed: 12/12/2022]
Abstract
Objective Loudness of dependence of the auditory evoked potential (LDAEP) is an electroencephalogram-based measure that represents amplitude changes of auditory evoked potentials in primary auditory cortex. Several narrative reviews argued that pre-treatment LDAEP values predict responses to selective serotonin reuptake inhibitors (SSRIs). This study aims to quantify the overall relationship between baseline LDAEP values and treatment response to SSRIs in patients with depression and generalized anxiety disorders, evidenced by clinical symptoms reductions, across multiple studies. Methods In our meta-analysis, seven articles with a total sample of 241 patients were included. Results Our results showed that stronger baseline LDAEP values predicted favorable response to SSRIs for depression and anxiety, with a moderate effect size. Conclusion The current results support the idea that LDAEP is a promising biomarker for SSRIs treatment prediction in patients with depression and generalized anxiety disorder.
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Affiliation(s)
- Sunkyung Yoon
- Department of Psychology, University of South Florida, Tampa, FL, USA
| | - Yourim Kim
- Clinical Emotion and Cognition Research Laboratory, Goyang, Korea
| | - Seung-Hwan Lee
- Clinical Emotion and Cognition Research Laboratory, Goyang, Korea.,Department of Psychiatry, Inje University Ilsan Paik Hospital, Goyang, Korea
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Rajkumar RP. Does Culture Influence Antidepressant Response? A Preliminary Investigation of Randomized Controlled Trials of Fluoxetine. Cureus 2021; 13:e15079. [PMID: 34017669 PMCID: PMC8129591 DOI: 10.7759/cureus.15079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Background Contemporary models of depression view the disorder as arising from an interaction between genetic vulnerability and adverse life experiences. The nature of these experiences is strongly influenced by social-cultural factors, and there is preliminary evidence that these factors may influence the response to treatment. Methods In this pilot study, pooled response rates obtained from 56 randomized controlled trials of fluoxetine for major depression, conducted across 21 countries, were analyzed in relation to Hofstede’s six dimensions of culture in these countries, while controlling for methodological quality. Results The cultural dimensions of power distance (r = .62, p = .002), masculinity (r = .45, p = .04) and indulgence (r = -.52, p = .016) were significantly correlated with antidepressant response rates, though only the first of these remained significant after correction for multiple comparisons. On linear regression analysis, the association between power distance and antidepressant response remained significant (β = .62, p = .002). Conclusions These preliminary results suggest that certain cultural factors may be significantly associated with cross-national variations in antidepressant response rates during clinical trials.
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Affiliation(s)
- Ravi P Rajkumar
- Psychiatry, Jawaharlal Institute of Postgraduate Medical Education and Research, Pondicherry, IND
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67
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Zhou S, Ma Q, Lou Y, Lv X, Tian H, Wei J, Zhang K, Zhu G, Chen Q, Si T, Wang G, Wang X, Zhang N, Huang Y, Liu Q, Yu X. Machine learning to predict clinical remission in depressed patients after acute phase selective serotonin reuptake inhibitor treatment. J Affect Disord 2021; 287:372-379. [PMID: 33836365 DOI: 10.1016/j.jad.2021.03.079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 03/22/2021] [Accepted: 03/24/2021] [Indexed: 01/10/2023]
Abstract
OBJECTIVE Selective serotonin reuptake inhibitors (SSRIs) are suggested as the first-line treatment for patients with major depressive disorder (MDD), but the remission rate is unsatisfactory. We aimed to establish machine learning models and explore variables available at baseline to predict the 8-week outcome among patients taking SSRIs. METHODS Data from 400 patients were used to build machine learnings. The last observation carried forward approach was used to determine the remitter/non-remitter status of the patients at week 8. Using least absolute shrinkage and selection operator (LASSO) to select features, we built 4 different machine learning algorithms including gradient boosting decision tree, support vector machine (SVM), random forests, and logistic regression with five-fold cross-validation. Then, we adopted Shapley additive explanations (SHAP) values to interpret the model output. RESULTS The remission rate is 67.8%. We obtained 78 features from the baseline characteristics, including 25 sociodemographic characteristics, 31 clinical features, 15 psychological traits and 7 neurocognitive functions, and 13 of these features were selected to establish SVM. The accuracy of the SVM prediction is 74.49%, reaching an average area under the curve of 0.734±0.043. The sensitivity is 0.899±0.038 with a positive predictive value of 0.776±0.028. The specificity is 0.422±0.091 with a negative predictive value of 0.674±0.086. According to the SHAP values, neurocognitive functions and anxiety and hypochondriasis symptoms were important predictors. CONCLUSION Our study supports the utilization of machine learning approaches with inexpensive and highly accessible variables to accurately predict the 8-week treatment outcome of SSRIs in patients with MDD.
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Affiliation(s)
- Shuzhe Zhou
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China; NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Qinhong Ma
- National Engineering Research Center for Software Engineering, Peking University, Beijing, China
| | - Yiwei Lou
- University of Science and Technology of China, Hefei, Anhui, China
| | - Xiaozhen Lv
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China; NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Hongjun Tian
- Nankai University Affiliated Anding Hospital, Tianjin Mental Health Center, Tianjin, China
| | - Jing Wei
- Department of Psychological Medicine, Peking Union Medical College Hospital, Beijing, China
| | - Kerang Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Gang Zhu
- Department of Psychiatry, The First Affiliated Hospital of China Medical University, Liaoning, China
| | - Qiaoling Chen
- Department of Psychiatry, Dalian Seventh People's Hospital, Dalian, China
| | - Tianmei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China; NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Gang Wang
- Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Xueyi Wang
- Department of Psychiatry, The First Hospital of Hebei Medical University, Mental Health Institute of Hebei Medical University, Shijiazhuang, China
| | - Nan Zhang
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yu Huang
- National Engineering Research Center for Software Engineering, Peking University, Beijing, China
| | - Qi Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China; NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China.
| | - Xin Yu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, China; NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China.
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68
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McIntyre RS, Rosenblat JD, Nemeroff CB, Sanacora G, Murrough JW, Berk M, Brietzke E, Dodd S, Gorwood P, Ho R, Iosifescu DV, Jaramillo CL, Kasper S, Kratiuk K, Lee JG, Lee Y, Lui LM, Mansur RB, Papakostas GI, Subramaniapillai M, Thase M, Vieta E, Young AH, Zarate CA, Stahl S. Synthesizing the Evidence for Ketamine and Esketamine in Treatment-Resistant Depression: An International Expert Opinion on the Available Evidence and Implementation. Am J Psychiatry 2021; 178:383-399. [PMID: 33726522 PMCID: PMC9635017 DOI: 10.1176/appi.ajp.2020.20081251] [Citation(s) in RCA: 299] [Impact Index Per Article: 99.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Replicated international studies have underscored the human and societal costs associated with major depressive disorder. Despite the proven efficacy of monoamine-based antidepressants in major depression, the majority of treated individuals fail to achieve full syndromal and functional recovery with the index and subsequent pharmacological treatments. Ketamine and esketamine represent pharmacologically novel treatment avenues for adults with treatment-resistant depression. In addition to providing hope to affected persons, these agents represent the first non-monoaminergic agents with proven rapid-onset efficacy in major depressive disorder. Nevertheless, concerns remain about the safety and tolerability of ketamine and esketamine in mood disorders. Moreover, there is uncertainty about the appropriate position of these agents in treatment algorithms, their comparative effectiveness, and the appropriate setting, infrastructure, and personnel required for their competent and safe implementation. In this article, an international group of mood disorder experts provides a synthesis of the literature with respect to the efficacy, safety, and tolerability of ketamine and esketamine in adults with treatment-resistant depression. The authors also provide guidance for the implementation of these agents in clinical practice, with particular attention to practice parameters at point of care. Areas of consensus and future research vistas are discussed.
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Affiliation(s)
- Roger S. McIntyre
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto; Department of Psychiatry, University of Toronto, Toronto; Department of Pharmacology, University of Toronto, Toronto; Brain and Cognition Discovery Foundation, Toronto
| | - Joshua D. Rosenblat
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto; Department of Psychiatry, University of Toronto, Toronto; Canadian Rapid Treatment Center of Excellence, Mississauga, Ontario
| | - Charles B. Nemeroff
- Department of Psychiatry and Behavioral Sciences, Austin Dell Medical School, University of Texas, Austin
| | - Gerard Sanacora
- Department of Psychiatry, Yale University School of Medicine, New Haven, Conn
| | - James W. Murrough
- Depression and Anxiety Center for Discovery and Treatment, Department of Psychiatry, and Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York
| | - Michael Berk
- Deakin University, Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia; Orygen, National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, University of Melbourne, Melbourne, Australia
| | - Elisa Brietzke
- Department of Psychiatry, Queen’s University School of Medicine, and Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario
| | - Seetal Dodd
- Deakin University, Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia; Centre for Youth Mental Health and Department of Psychiatry, University of Melbourne, Melbourne, Australia
| | - Philip Gorwood
- Université de Paris, Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, and GHU Paris Psychiatrie et Neurosciences, CMME, Hôpital Sainte-Anne, Paris
| | - Roger Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, and Institute of Health Innovation and Technology, National University of Singapore, Singapore
| | - Dan V. Iosifescu
- Department of Psychiatry, NYU School of Medicine, and Clinical Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York
| | | | | | - Kevin Kratiuk
- Canadian Rapid Treatment Center of Excellence, Mississauga, Ontario; Department of Clinical Immunology, Poznan University of Medical Sciences, Poznan, Poland
| | - Jung Goo Lee
- Department of Psychiatry, College of Medicine, Haeundae Paik Hospital, Paik Institute for Clinical Research, and Department of Health Science and Technology, Graduate School, Inje University, Busan, Republic of Korea
| | - Yena Lee
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto; Institute of Medical Science, University of Toronto, Toronto
| | - Leanna M.W. Lui
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto
| | - Rodrigo B. Mansur
- Mood Disorders Psychopharmacology Unit, University Health Network, Toronto; Department of Psychiatry, University of Toronto, Toronto
| | | | | | - Michael Thase
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, and Corporal Michael J. Crescenz VA Medical Center, Philadelphia
| | - Eduard Vieta
- Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona
| | - Allan H. Young
- Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London and South London, and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Beckenham, Kent
| | - Carlos A. Zarate
- Experimental Therapeutics and Pathophysiology Branch and Section on the Neurobiology and Treatment of Mood Disorders, Division of Intramural Research Program, NIMH, Bethesda, Md
| | - Stephen Stahl
- Department of Psychiatry and Neuroscience, University of California, Riverside, and University of California, San Diego
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Mi K, Guo Q, Xu BY, Wang M, Bi H. Efficacy of hyperbaric oxygen combined with escitalopram in depression and its effect on cognitive function. Pak J Med Sci 2021; 37:1054-1057. [PMID: 34290782 PMCID: PMC8281171 DOI: 10.12669/pjms.37.4.3993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 02/22/2021] [Accepted: 02/27/2021] [Indexed: 11/18/2022] Open
Abstract
Objective: To investigate the efficacy of hyperbaric oxygen (HBO) combined with escitalopram in patients with depression and its effect on cognitive function. Methods: From 2016 to 2018, seventy patients with depression aged 18-65 years treated in Affiliated Hospital of Hebei University were selected. Seventy patients with depression meeting the diagnostic criteria of ICD-10 were selected and randomly divided into control group and observation group using a random number table, with 35 patients in each group. The control group was treated with escitalopram, while the observation group was additionally treated with HBO on this basis. The patients were assessed using the Hamilton Depression Scale (HAMD) and Montreal Cognitive Assessment Scale (MoCA) before treatment and two, four and six weeks after treatment. Results: Two weeks after treatment, HAMD score showed a statistically significant difference between the two groups (P < 0.05). No statistically significant differences were found in HAMD score between the two groups four and six weeks after treatment (P > 0.05). Four and six weeks after treatment, MoCA score presented statistically significant differences between the two groups (P < 0.05). Conclusion: Escitalopram combined with HBO in the treatment of depression presents rapid efficacy and a certain effect in improving cognitive function.
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Affiliation(s)
- Kun Mi
- Kun Mi, The Second Department of Affective Disorders, The Sixth People's Hospital of Hebei Province, Baoding, Hebei, 071000, P.R. China
| | - Qiang Guo
- Qiang Guo, Department of Thoracic Surgery, Affiliated Hospital of Hebei University, Baoding, Hebei 071000, P.R. China
| | - Bao-Yan Xu
- Bao-yan Xu, The Second Department of Affective Disorders, The Sixth People's Hospital of Hebei Province, Baoding, Hebei, 071000, P.R. China
| | - Man Wang
- Man Wang, The Second Department of Affective Disorders, The Sixth People's Hospital of Hebei Province, Baoding, Hebei, 071000, P.R. China
| | - Hao Bi
- Hao Bi, The Second Department of Affective Disorders, The Sixth People's Hospital of Hebei Province, Baoding, Hebei, 071000, P.R. China
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Flint AJ, Bingham KS, Neufeld NH, Alexopoulos GS, Mulsant BH, Rothschild AJ, Whyte EM, Voineskos AN, Marino P, Meyers BS. Association between psychomotor disturbance and treatment outcome in psychotic depression: a STOP-PD II report. Psychol Med 2021; 52:1-7. [PMID: 33766150 DOI: 10.1017/s0033291721000805] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Little is known about the relationship between psychomotor disturbance (PMD) and treatment outcome of psychotic depression. This study examined the association between PMD and subsequent remission and relapse of treated psychotic depression. METHODS Two hundred and sixty-nine men and women aged 18-85 years with an episode of psychotic depression were treated with open-label sertraline plus olanzapine for up to 12 weeks. Participants who remained in remission or near-remission following an 8-week stabilization phase were eligible to participate in a 36-week randomized controlled trial (RCT) that compared the efficacy and tolerability of sertraline plus olanzapine (n = 64) with sertraline plus placebo (n = 62). PMD was measured with the psychiatrist-rated sign-based CORE at acute phase baseline and at RCT baseline. Spearman's correlations and logistic regression analyses were used to analyze the association between CORE total score at acute phase baseline and remission/near-remission and CORE total score at RCT baseline and relapse. RESULTS Higher CORE total score at acute phase baseline was associated with lower frequency of remission/near-remission. Higher CORE total score at RCT baseline was associated with higher frequency of relapse, in the RCT sample as a whole, as well as in each of the two randomized groups. CONCLUSIONS PMD is associated with poorer outcome of psychotic depression treated with sertraline plus olanzapine. Future research needs to examine the neurobiology of PMD in psychotic depression in relation to treatment outcome.
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Affiliation(s)
- Alastair J Flint
- The Department of Psychiatry, University of Toronto, Toronto, Canada
- Centre for Mental Health, University Health Network, Toronto, Canada
| | - Kathleen S Bingham
- The Department of Psychiatry, University of Toronto, Toronto, Canada
- Centre for Mental Health, University Health Network, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
| | - Nicholas H Neufeld
- The Department of Psychiatry, University of Toronto, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
| | - George S Alexopoulos
- Department of Psychiatry, Weill Cornell Medicine of Cornell University and New York Presbyterian Hospital, Westchester Division, New York, NY, USA
| | - Benoit H Mulsant
- The Department of Psychiatry, University of Toronto, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
| | - Anthony J Rothschild
- University of Massachusetts Medical School and UMass Memorial Health Care, Worcester, MA, USA
| | - Ellen M Whyte
- Department of Psychiatry, University of Pittsburgh School of Medicine and UPMC Western Psychiatric Hospital, Pittsburgh, PA, USA
| | - Aristotle N Voineskos
- The Department of Psychiatry, University of Toronto, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
| | - Patricia Marino
- Department of Psychiatry, Weill Cornell Medicine of Cornell University and New York Presbyterian Hospital, Westchester Division, New York, NY, USA
| | - Barnett S Meyers
- Department of Psychiatry, Weill Cornell Medicine of Cornell University and New York Presbyterian Hospital, Westchester Division, New York, NY, USA
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71
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Serotonin 2A receptor polymorphism rs3803189 mediated by dynamics of default mode network: a potential biomarker for antidepressant early response. J Affect Disord 2021; 283:130-138. [PMID: 33548906 DOI: 10.1016/j.jad.2021.01.047] [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: 11/23/2020] [Revised: 01/15/2021] [Accepted: 01/20/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND Serotonin 2A receptors (HTR2A) play a crucial role in the therapeutic response to antidepressant. The activity of serotonergic system could modulate the connectivity of the default mode network (DMN) in human brain. Our research investigated the influence of the single nucleotide polymorphism (SNP) of HTR2A on the early treatment response of antidepressant and their relation to dynamic changes of DMN for the first time. METHODS A total of 134 major depressive disorder patients and 95 healthy controls from two independent datasets were enrolled. All subjects have genotyped candidate HTR2A polymorphisms, dynamic brain parameters flexibility and integration were calculated according to the resting-state functional magnetic resonance imaging (rs-fMRI) at baseline. Patients received selective serotonin reuptake inhibitors (SSRIs) treatment with conventional dose in the next two weeks. RESULTS We found the correlation of the risk-associated variant belonged to HTR2A polymorphism rs3803189 with the achievements of antidepressant early response, and also with the stronger dynamic changes of DMN. Further mediation analysis indicated that the bond between rs3803189 and antidepressant early response was mediated by the integration between the right angular gyrus (AG.R) and the subcortical network (SCN), which were validated over both the main and replication datasets. LIMITATIONS Except the AG.R-SCN circuit, other factors which influence the relationship between rs3803189 and antidepressant therapy deserve to be explored further. Besides, heterogeneity of samples limited the power of the current result. CONCLUSIONS Our findings provided a potential biomarker for individual treatment sensitivity and produced positive effects on revealing the complicated gene-brain-disorder relationship.
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72
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Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis. Transl Psychiatry 2021; 11:168. [PMID: 33723229 PMCID: PMC7960732 DOI: 10.1038/s41398-021-01286-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 02/05/2021] [Accepted: 02/18/2021] [Indexed: 02/06/2023] Open
Abstract
No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81-0.87), sensitivity of 77% (95% CI 71-82), and specificity of 79% (95% CI 73-84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.
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73
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Circadian depression: A mood disorder phenotype. Neurosci Biobehav Rev 2021; 126:79-101. [PMID: 33689801 DOI: 10.1016/j.neubiorev.2021.02.045] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 02/18/2021] [Accepted: 02/28/2021] [Indexed: 12/15/2022]
Abstract
Major mood syndromes are among the most common and disabling mental disorders. However, a lack of clear delineation of their underlying pathophysiological mechanisms is a major barrier to prevention and optimised treatments. Dysfunction of the 24-h circadian system is a candidate mechanism that has genetic, behavioural, and neurobiological links to mood syndromes. Here, we outline evidence for a new clinical phenotype, which we have called 'circadian depression'. We propose that key clinical characteristics of circadian depression include disrupted 24-h sleep-wake cycles, reduced motor activity, low subjective energy, and weight gain. The illness course includes early age-of-onset, phenomena suggestive of bipolarity (defined by bidirectional associations between objective motor and subjective energy/mood states), poor response to conventional antidepressant medications, and concurrent cardiometabolic and inflammatory disturbances. Identifying this phenotype could be clinically valuable, as circadian-targeted strategies show promise for reducing depressive symptoms and stabilising illness course. Further investigation of underlying circadian disturbances in mood syndromes is needed to evaluate the clinical utility of this phenotype and guide the optimal use of circadian-targeted interventions.
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74
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Czéh B, Simon M. Benefits of animal models to understand the pathophysiology of depressive disorders. Prog Neuropsychopharmacol Biol Psychiatry 2021; 106:110049. [PMID: 32735913 DOI: 10.1016/j.pnpbp.2020.110049] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 07/13/2020] [Accepted: 07/21/2020] [Indexed: 12/14/2022]
Abstract
Major depressive disorder (MDD) is a potentially life-threatening mental disorder imposing severe social and economic burden worldwide. Despite the existence of effective antidepressant treatment strategies the exact pathophysiology of the disease is still unknown. Large number of animal models of MDD have been developed over the years, but all of them suffer from significant shortcomings. Despite their limitations these models have been extensively used in academic research and drug development. The aim of this review is to highlight the benefits of animal models of MDD. We focus here on recent experimental data where animal models were used to examine current theories of this complex disease. We argue, that despite their evident imperfections, these models provide invaluable help to understand cellular and molecular mechanisms contributing to the development of MDD. Furthermore, animal models are utilized in research to find clinically useful biomarkers. We discuss recent neuroimaging and microRNA studies since these investigations yielded promising candidates for biomarkers. Finally, we briefly summarize recent progresses in drug development, i.e. the FDA approval of two novel antidepressant drugs: S-ketamine and brexanolone (allopregnanolone). Deeper understanding of the exact molecular and cellular mechanisms of action responsible for the antidepressant efficacy of these rapid acting drugs could aid us to design further compounds with similar effectiveness, but less side effects. Animal studies are likely to provide valuable help in this endeavor.
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Affiliation(s)
- Boldizsár Czéh
- Neurobiology of Stress Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary; Department of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary.
| | - Maria Simon
- Neurobiology of Stress Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary; Department of Psychiatry and Psychotherapy, Medical School, University of Pécs, Hungary
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75
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Zanardi R, Prestifilippo D, Fabbri C, Colombo C, Maron E, Serretti A. Precision psychiatry in clinical practice. Int J Psychiatry Clin Pract 2021; 25:19-27. [PMID: 32852246 DOI: 10.1080/13651501.2020.1809680] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The treatment of depression represents a major challenge for healthcare systems and choosing among the many available drugs without objective guidance criteria is an error-prone process. Recently, pharmacogenetic biomarkers entered in prescribing guidelines, giving clinicians the possibility to use this additional tool to guide prescription and improve therapeutic outcomes. This marked an important step towards precision psychiatry, which aim is to integrate biological and environmental information to personalise treatments. Only genetic variants in cytochrome enzymes are endorsed by prescribing guidelines, but in the future polygenic predictors of treatment outcomes may be translated into the clinic. The integration of genetics with other relevant information (e.g., concomitant diseases and treatments, drug plasma levels) could be managed in a standardised way through ad hoc software. The overcoming of the current obstacles (e.g., staff training, genotyping and informatics facilities) can lead to a broad implementation of precision psychiatry and represent a revolution for psychiatric care.Key pointsPrecision psychiatry aims to integrate biological and environmental information to personalise treatments and complement clinical judgementPharmacogenetic biomarkers in cytochrome genes were included in prescribing guidelines and represented an important step towards precision psychiatryTherapeutic drug monitoring is an important and cost-effective tool which should be integrated with genetic testing and clinical evaluation in order to optimise pharmacotherapyOther individual factors relevant to pharmacotherapy response (e.g., individual's symptom profile, concomitant diseases) can be integrated with genetic information through artificial intelligence to provide treatment recommendationsThe creation of pharmacogenetic services within healthcare systems is a challenging and multi-step process, education of health professionals, promotion by institutions and regulatory bodies, economic and ethical barriers are the main issues.
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Affiliation(s)
- Raffaella Zanardi
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Department of Clinical Neurosciences, University Vita-Salute San Raffaele, Milan, Italy
| | - Dario Prestifilippo
- Department of Clinical Neurosciences, University Vita-Salute San Raffaele, Milan, Italy
| | - Chiara Fabbri
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Cristina Colombo
- Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Department of Clinical Neurosciences, University Vita-Salute San Raffaele, Milan, Italy
| | - Eduard Maron
- Department of Psychiatry, University of Tartu, Tartu, Estonia.,Division of Brain Sciences, Department of Medicine, Faculty of Medicine, Centre for Neuropsychopharmacology, Imperial College London, London, UK.,Documental Ltd, Tallinn, Estonia
| | - Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
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Xue L, Pei C, Wang X, Wang H, Tian S, Yao Z, Lu Q. Predicting Neuroimaging Biomarkers for Antidepressant Selection in Early Treatment of Depression. J Magn Reson Imaging 2021; 54:551-559. [PMID: 33634921 DOI: 10.1002/jmri.27577] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 02/09/2021] [Accepted: 02/10/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Due to the biological heterogeneity, 60%-70% of patients with major depressive disorder (MDD) do not respond to or achieve remission from first-line antidepressants. Predicting neuroimaging biomarkers for early antidepressant treatment could guide initial antidepressant therapy. PURPOSE To assess for neuroimaging biomarkers for antidepressant selection in early antidepressant treatment. STUDY TYPE Prospective. SUBJECTS A total of 85 MDD patients from the major site and 33 MDD patients from an out-of-sample test site. FIELD STRENGTH/SEQUENCE A 3.0 T, T1-weighted imaging using a magnetization-prepared rapid acquisition gradient-echo sequence and diffusion tensor imaging (DTI) using an echo-planar sequence. ASSESSMENT Baseline DTI data of patients who achieved early improvement after 2-weeks of antidepressant treatment (selective serotonin reuptake inhibitors [SSRI] or serotonin-norepinephrine reuptake inhibitors [SNRI]) were analyzed. An ensemble model was constructed using data from the major site and then applied to assess the early response of patients at the out-of-sample test site. STATISTICAL TESTS Support vector machine combined with leave-one-out cross-validation were applied to construct the whole model from individual base models from different brain regions. Discriminative biomarkers were evaluated by calculating the changes in sensitivity and specificity obtained when removing a single base model from the whole model, the base model being removed changing in each run. RESULTS Training performance over MDD patients at the major site achieved 75% accuracy while performance with accuracy of 70% was achieved in the out-of-sample test site. Assessing sensitivity and specificity changes following the removal of single base models from the prominent model highlighted the functions of two neural circuitries: SSRI-related emotion regulation circuitry, centered on the hippocampus (sensitivity changes: 10%) and amygdala (sensitivity changes: 11%); and SNRI-related emotion and reward circuitry, centered on the putamen (specificity changes: 8%) and orbital part of superior frontal gyrus (specificity changes: 12%). DATA CONCLUSION These findings support future research on clinical antidepressant selection for MDD. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Li Xue
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, 210096, China
| | - Cong Pei
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, 210096, China
| | - Xinyi Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, 210096, China
| | - Huan Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, 210096, China
| | - Shui Tian
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, 210096, China
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, 210096, China
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Grieco SF, Qiao X, Johnston KG, Chen L, Nelson RR, Lai C, Holmes TC, Xu X. Neuregulin signaling mediates the acute and sustained antidepressant effects of subanesthetic ketamine. Transl Psychiatry 2021; 11:144. [PMID: 33627623 PMCID: PMC7904825 DOI: 10.1038/s41398-021-01255-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/09/2021] [Accepted: 02/01/2021] [Indexed: 01/03/2023] Open
Abstract
Subanesthetic ketamine evokes rapid antidepressant effects in human patients that persist long past ketamine's chemical half-life of ~2 h. Ketamine's sustained antidepressant action may be due to modulation of cortical plasticity. We find that ketamine ameliorates depression-like behavior in the forced swim test in adult mice, and this depends on parvalbumin-expressing (PV) neuron-directed neuregulin-1 (NRG1)/ErbB4 signaling. Ketamine rapidly downregulates NRG1 expression in PV inhibitory neurons in mouse medial prefrontal cortex (mPFC) following a single low-dose ketamine treatment. This NRG1 downregulation in PV neurons co-tracks with the decreases in synaptic inhibition to mPFC excitatory neurons for up to a week. This results from reduced synaptic excitation to PV neurons, and is blocked by exogenous NRG1 as well as by PV targeted ErbB4 receptor knockout. Thus, we conceptualize that ketamine's effects are mediated through rapid and sustained cortical disinhibition via PV-specific NRG1 signaling. Our findings reveal a novel neural plasticity-based mechanism for ketamine's acute and long-lasting antidepressant effects.
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Affiliation(s)
- Steven F. Grieco
- grid.266093.80000 0001 0668 7243Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697-1275 USA
| | - Xin Qiao
- grid.266093.80000 0001 0668 7243Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697-1275 USA
| | - Kevin G. Johnston
- grid.266093.80000 0001 0668 7243Department of Mathematics, University of California, Irvine, CA 92697-3875 USA
| | - Lujia Chen
- grid.266093.80000 0001 0668 7243Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697-1275 USA
| | - Renetta R. Nelson
- grid.266093.80000 0001 0668 7243Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697-1275 USA
| | - Cary Lai
- grid.411377.70000 0001 0790 959XDepartment of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405-7000 USA
| | - Todd C. Holmes
- grid.19006.3e0000 0000 9632 6718Department of Physiology and Biophysics, School of Medicine, Universityof California, Irvine, CA 92697- 4560 USA ,grid.266093.80000 0001 0668 7243The Center for Neural Circuit Mapping, University of California, Irvine, CA 92697 USA
| | - Xiangmin Xu
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA, 92697-1275, USA. .,The Center for Neural Circuit Mapping, University of California, Irvine, CA, 92697, USA. .,Department of Biomedical Engineering, University of California, Irvine, CA, 92697-2715, USA. .,Department of Microbiology and Molecular Genetics, University of California, Irvine, CA, 92697-4025, USA. .,Department of Computer Science, University of California, Irvine, CA, 92697-3435, USA.
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Nakamura T, Tomita M, Horikawa N, Ishibashi M, Uematsu K, Hiraki T, Abe T, Uchimura N. Functional connectivity between the amygdala and subgenual cingulate gyrus predicts the antidepressant effects of ketamine in patients with treatment-resistant depression. Neuropsychopharmacol Rep 2021; 41:168-178. [PMID: 33615749 PMCID: PMC8340826 DOI: 10.1002/npr2.12165] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 01/21/2021] [Accepted: 01/23/2021] [Indexed: 12/28/2022] Open
Abstract
Aim Approximately one‐third of patients with major depressive disorder develop treatment‐resistant depression. One‐third of patients with treatment‐resistant depression demonstrate resistance to ketamine, which is a novel antidepressant effective for this disorder. The objective of this study was to examine the utility of resting‐state functional magnetic resonance imaging for the prediction of treatment response to ketamine in treatment‐resistant depression. Methods An exploratory seed‐based resting‐state functional magnetic resonance imaging analysis was performed to examine baseline resting‐state functional connectivity differences between ketamine responders and nonresponders before treatment with multiple intravenous ketamine infusions. Results Fifteen patients with treatment‐resistant depression received multiple intravenous subanesthetic (0.5 mg/kg/40 minutes) ketamine infusions, and nine were identified as responders. The exploratory resting‐state functional magnetic resonance imaging analysis identified a cluster of significant baseline resting‐state functional connectivity differences associating ketamine response between the amygdala and subgenual anterior cingulate gyrus in the right hemisphere. Using anatomical region of interest analysis of the resting‐state functional connectivity, ketamine response was predicted with 88.9% sensitivity and 100% specificity. The resting‐state functional connectivity of significant group differences between responders and nonresponders retained throughout the treatment were considered a trait‐like feature of heterogeneity in treatment‐resistant depression. Conclusion This study suggests the possible clinical utility of resting‐state functional magnetic resonance imaging for predicting the antidepressant effects of ketamine in treatment‐resistant depression patients and implicated resting‐state functional connectivity alterations to determine the trait‐like pathophysiology underlying treatment response heterogeneity in treatment‐resistant depression. This study illustrates that the alteration in the RSFC within the right AN in TRD patients reflects the antidepressant response to ketamine at baseline. The alteration remained throughout the 2‐week treatment with multiple ketamine infusions and seemed to reflect the trait‐like features underlying treatment heterogeneity in TRD. By employing an anatomical ROI of the sc/sgACC, the present study also suggests the possible clinical utility of the rsfMRI to predict the treatment response to ketamine in TRD patients.
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Affiliation(s)
- Tomoyuki Nakamura
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan
| | - Masaru Tomita
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan.,Elm-tree Mental Clinic, Ogori City, Japan
| | - Naoki Horikawa
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan.,Nozoe Hills Hospital, Kurume City, Japan
| | - Masatoshi Ishibashi
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan
| | - Ken Uematsu
- Uematsu Mental Clinic, Chikugo City, Japan.,Department of Pharmacology, Kurume University School of Medicine, Kurume City, Japan
| | - Teruyuki Hiraki
- Department of Anaesthesiology, Kurume University School of Medicine, Kurume City, Japan
| | - Toshi Abe
- Department of Radiology, Kurume University School of Medicine, Kurume City, Japan
| | - Naohisa Uchimura
- Department of Neuropsychiatry, Kurume University School of Medicine, Kurume City, Japan
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79
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Treatment-Resistant Depression Revisited: A Glimmer of Hope. J Pers Med 2021; 11:jpm11020155. [PMID: 33672126 PMCID: PMC7927134 DOI: 10.3390/jpm11020155] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 02/18/2021] [Accepted: 02/18/2021] [Indexed: 12/20/2022] Open
Abstract
Major Depressive Disorder (MDD) is a highly prevalent psychiatric disorder worldwide. It causes individual suffering, loss of productivity, increased health care costs and high suicide risk. Current pharmacologic interventions fail to produce at least partial response to approximately one third of these patients, and remission is obtained in approximately 30% of patients. This is known as Treatment-Resistant Depression (TRD). The burden of TRD exponentially increases the longer it persists, with a higher risk of impaired functional and social functioning, vast losses in quality of life and significant risk of somatic morbidity and suicidality. Different approaches have been suggested and utilized, but the results have not been encouraging. In this review article, we present new approaches to identify and correct potential causes of TRD, thereby reducing its prevalence and with it the overall burden of this disease entity. We will address potential contributory factors to TRD, most of which can be investigated in many laboratories as routine tests. We discuss endocrinological aberrations, notably, hypothalamic-pituitary-adrenal (HPA) axis dysregulation and thyroid and gonadal dysfunction. We address the role of Vitamin D in contributing to depression. Pharmacogenomic testing is being increasingly used to determine Single Nucleotide Polymorphisms in Cytochrome P450, Serotonin Transporter, COMT, folic acid conversion (MTHFR). As the role of immune system dysregulation is being recognized as potentially a major contributory factor to TRD, the measurement of C-reactive protein (CRP) and select immune biomarkers, where testing is available, can guide combination treatments with anti-inflammatory agents (e.g., selective COX-2 inhibitors) reversing treatment resistance. We focus on established and emerging test procedures, potential biomarkers and non-biologic assessments and interventions to apply personalized medicine to effectively manage treatment resistance in general and TRD specifically.
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80
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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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81
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Jones BDM, Husain MI, Mulsant BH. The use of sequential pharmacotherapy for the treatment of acute major depression: a scoping review. Expert Opin Pharmacother 2021; 22:1005-1014. [PMID: 33612048 DOI: 10.1080/14656566.2021.1878144] [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] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Major Depressive Disorder (MDD) is a chronic, relapsing, and remitting disorder affecting over 250 million persons each year worldwide. More than 50% of the patients do not respond to their initial antidepressant treatment and may benefit from sequential pharmacotherapy for the acute treatment of their MDD. Although guidelines outline options for next-step treatments, there is a paucity of evidence to select specific second- or third-step treatments. AREAS COVERED This scoping review synthesizes and discusses available evidence for sequential pharmacotherapy for MDD. MEDLINE was searched from inception to 7 July 2020; 4490 studies were identified. We selected meta-analyses and reports on clinical trials that were judged to inform the sequential selection of pharmacotherapy for MDD. EXPERT OPINION Most relevant published trials are focused on, and support, the use of augmentation pharmacotherapy. There is also some support for other strategies such as combining or switching antidepressants. In the future, more studies need to directly compare these sequential options. To provide more personalized treatment within the framework of precision psychiatry, these studies should include an assessment of moderators and mediators ('mechanism') of antidepressant response.
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Affiliation(s)
- Brett D M Jones
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - M Ishrat Husain
- Department of Psychiatry, University of Toronto, Toronto, Canada.,General Adult Psychiatry and Health Systems Division, Centre for Addiction and Mental Health, Toronto, Canada
| | - Benoit H Mulsant
- Department of Psychiatry, University of Toronto, Toronto, Canada.,Adult Neurodevelopmental and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, Canada
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82
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Mncube K, Möller M, Harvey BH. Post-weaning Social Isolated Flinders Sensitive Line Rats Display Bio-Behavioural Manifestations Resistant to Fluoxetine: A Model of Treatment-Resistant Depression. Front Psychiatry 2021; 12:688150. [PMID: 34867504 PMCID: PMC8635751 DOI: 10.3389/fpsyt.2021.688150] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 09/29/2021] [Indexed: 12/16/2022] Open
Abstract
Treatment-resistant depression (TRD) complicates the management of major depression (MD). The underlying biology of TRD involves interplay between genetic propensity and chronic and/or early life adversity. By combining a genetic animal model of MD and post-weaning social isolation rearing (SIR), we sought to produce an animal that displays more severe depressive- and social anxiety-like manifestations resistant to standard antidepressant treatment. Flinders Sensitive Line (FSL) pups were social or isolation reared from weaning [postnatal day (PND) 21], receiving fluoxetine (FLX) from PND 63 (10 mg/kg × 14 days), and compared to Sprague Dawley (SD) controls. Depressive-, anxiety-like, and social behaviour were assessed from PND 72 in the forced swim test (FST) and social interaction test (SIT). Post-mortem cortico-hippocampal norepinephrine (NE), serotonin (5-HT), and dopamine (DA), as well as plasma interleukin 6 (IL-6), tumour necrosis factor alpha (TNF-α), corticosterone (CORT), and dopamine-beta-hydroxylase (DBH) levels were assayed. FSL rats displayed significant cortico-hippocampal monoamine disturbances, and depressive- and social anxiety-like behaviour, the latter two reversed by FLX. SIR-exposed FSL rats exhibited significant immobility in the FST and social impairment which were, respectively, worsened by or resistant to FLX. In SIR-exposed FSL rats, FLX significantly raised depleted NE and 5-HT, significantly decreased DBH and caused a large effect size increase in DA and decrease in CORT and TNF-α. Concluding, SIR-exposed FSL rats display depressive- and social anxiety-like symptoms that are resistant to, or worsened by, FLX, with reduced plasma DBH and suppressed cortico-hippocampal 5-HT, NE and DA, all variably altered by FLX. Exposure of a genetic animal model of MD to post-weaning SIR results in a more intractable depressive-like phenotype as well as changes in TRD-related biomarkers, that are resistant to traditional antidepressant treatment. Given the relative absence of validated animal models of TRD, these findings are especially promising and warrant study, especially further predictive validation.
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Affiliation(s)
- Khulekani Mncube
- Centre of Excellence for Pharmaceutical Sciences (PharmaCen), Division of Pharmacology, School of Pharmacy, North-West University, Potchefstroom, South Africa
| | - Marisa Möller
- Centre of Excellence for Pharmaceutical Sciences (PharmaCen), Division of Pharmacology, School of Pharmacy, North-West University, Potchefstroom, South Africa
| | - Brian H Harvey
- Centre of Excellence for Pharmaceutical Sciences (PharmaCen), Division of Pharmacology, School of Pharmacy, North-West University, Potchefstroom, South Africa.,South African Medical Research Council Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Mental Health and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
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83
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Han KM, Ham BJ, Kim YK. Development of Neuroimaging-Based Biomarkers in Major Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1305:85-99. [PMID: 33834396 DOI: 10.1007/978-981-33-6044-0_6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
A leading goal in the field of biological psychiatry for depression is to find a promising diagnostic biomarker and selection of specific psychiatric treatment mode that is most likely to benefit patients with depression. Recent neuroimaging studies have characterized the pathophysiology of major depressive disorder (MDD) with functional and structural alterations in the neural circuitry involved in emotion or reward processing. Particularly, structural and functional magnetic resonance imaging (MRI) studies have reported that the brain structures deeply involved in emotion regulation or reward processing including the amygdala, prefrontal cortex (PFC), anterior cingulate cortex (ACC), ventral striatum, and hippocampus are key regions that provide useful information about diagnosis and treatment outcome prediction in MDD. For example, it has been consistently reported that elevated activity of the ACC is associated with better antidepressant response in patients with MDD. This chapter will discuss a growing body of evidence that suggests that diagnosis or prediction of outcome for specific treatment can be assisted by a neuroimaging-based biomarker in MDD.
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Affiliation(s)
- Kyu-Man Han
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Yong-Ku Kim
- Department of Psychiatry, Korea University Ansan Hospital, College of Medicine, Ansan, Republic of Korea.
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84
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Liu G, Jiao K, Zhong Y, Hao Z, Wang C, Xu H, Teng C, Song X, Xiao C, Fox PT, Zhang N, Wang C. The alteration of cognitive function networks in remitted patients with major depressive disorder: an independent component analysis. Behav Brain Res 2020; 400:113018. [PMID: 33301816 DOI: 10.1016/j.bbr.2020.113018] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 07/22/2020] [Accepted: 11/11/2020] [Indexed: 02/08/2023]
Abstract
INTRODUCTION Dysfunctional connectivity of resting-state functional networks has been observed in patients with major depressive disorder (MDD), particularly in cognitive function networks including the central executive network (CEN), default mode network (DMN) and salience network (SN). Findings from studies examining how aberrant functional connectivity (FC) changed after antidepressant treatment, however, have been inconsistent. Thus, the purpose of the present study was to explore potential mechanisms of altered cognitive function networks during resting-state between remitted major depressive disorder (rMDD) patients and healthy controls (HCs) and furthermore, the relationship between dysfunctional connectivity patterns in rMDD and clinical symptoms. METHODOLOGY In this study, 19 HCs and 19 rMDD patients were recruited for resting-state functional magnetic resonance imaging (fMRI) scanning. FC was evaluated with independent component analysis for CEN, DMN and SN. Two sample t tests were conducted to compare differences between rMDD and HCs. A Pearson correlation analysis was also performed to examine the relationship between connectivity of networks and cognitive function scores and clinical symptoms. RESULTS Compared to healthy controls, remitted patients showed lower connectivity in CEN, mostly in the superior frontal gyrus (SFG), middle frontal gyrus (MFG), inferior parietal lobule (IPL) and part of the supramarginal gyrus (SMG). Conversely, the bilateral insula, part of the SMG (a key node of the CEN) and dorsal anterior cingulate cortex (dACC) of the DMN showed higher connectivity in rMDD patients. Pearson correlation results demonstrated that connectivity of the right IPL in CEN was positively correlated with cognitive function scores, and connectivity of the left insula was negatively correlated with BDI scores. CONCLUSIONS Though rMDD patients reached the standard of clinal remission, unique impairments of FC in cognitive function networks remained. Aberrant FC between cognitive function networks responsible for executive control was observed in rMDD and may be associated with residual clinical symptoms.
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Affiliation(s)
- Gang Liu
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China
| | - Kaili Jiao
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Zhengzhou Ninth People's Hospital, Zhengzhou, China
| | - Yuan Zhong
- School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China; Jiangsu Key Laboratory of Mental Health and Cognitive Science, Nanjing Normal University, Nanjing 210097, China
| | - Ziyu Hao
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China
| | - Chiyue Wang
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China
| | - Huazhen Xu
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China
| | - Changjun Teng
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xiu Song
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chaoyong Xiao
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China
| | - Peter T Fox
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; South Texas Veterans Healthcare System, University of Texas Health San Antonio, United States; Research Imaging Institute, University of Texas Health San Antonio, United States
| | - Ning Zhang
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Chun Wang
- Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, Jiangsu, China; Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, Jiangsu, China; Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, Jiangsu, China; School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.
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85
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Kopf-Beck J, Zimmermann P, Egli S, Rein M, Kappelmann N, Fietz J, Tamm J, Rek K, Lucae S, Brem AK, Sämann P, Schilbach L, Keck ME. Schema therapy versus cognitive behavioral therapy versus individual supportive therapy for depression in an inpatient and day clinic setting: study protocol of the OPTIMA-RCT. BMC Psychiatry 2020; 20:506. [PMID: 33054737 PMCID: PMC7557007 DOI: 10.1186/s12888-020-02880-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 09/19/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Major depressive disorder represents (MDD) a major cause of disability and disease burden. Beside antidepressant medication, psychotherapy is a key approach of treatment. Schema therapy has been shown to be effective in the treatment of psychiatric disorders, especially personality disorders, in a variety of settings and patient groups. Nevertheless, there is no evidence on its effectiveness for MDD in an inpatient nor day clinic setting and little is known about the factors that drive treatment response in such a target group. METHODS In the current protocol, we outline OPTIMA (OPtimized Treatment Identification at the MAx Planck Institute): a single-center randomized controlled trial of schema therapy as a treatment approach for MDD in an inpatient and day clinic setting. Over the course of 7 weeks, we compare schema therapy with cognitive behavioral therapy and individual supportive therapy, conducted in individual and group sessions and with no restrictions regarding concurrent antidepressant medication, thus approximating real-life treatment conditions. N = 300 depressed patients are included. All study therapists undergo a specific training and supervision and therapy adherence is assessed. Primary outcome is depressive symptom severity as self-assessment (Beck Depression Inventory-II) and secondary outcomes are clinical ratings of MDD (Montgomery-Asberg Depression Rating Scale), recovery rates after 7 weeks according to the Munich-Composite International Diagnostic Interview, general psychopathology (Brief Symptom Inventory), global functioning (World Health Organization Disability Assessment Schedule), and clinical parameters such as dropout rates. Further parameters on a behavioral, cognitive, psychophysiological, and biological level are measured before, during and after treatment and in 2 follow-up assessments after 6 and 24 months after end of treatment. DISCUSSION To our knowledge, the OPTIMA-Trial is the first to investigate the effectiveness of schema therapy as a treatment approach of MDD, to investigate mechanisms of change, and explore predictors of treatment response in an inpatient and day clinic setting by using such a wide range of parameters. Insights from OPTIMA will allow more integrative approaches of psychotherapy of MDD. Especially, the identification of intervention-specific markers of treatment response can improve evidence-based clinical decision for individualizing treatment. TRIAL REGISTRATION Identifier on clinicaltrials.gov : NCT03287362 ; September, 12, 2017.
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Affiliation(s)
- Johannes Kopf-Beck
- Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany.
| | - Petra Zimmermann
- Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany
| | - Samy Egli
- Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany
| | - Martin Rein
- Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany
| | - Nils Kappelmann
- Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Julia Fietz
- Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Jeanette Tamm
- Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany
| | - Katharina Rek
- Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany
- University of Kassel, Kassel, Germany
| | - Susanne Lucae
- Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany
| | - Anna-Katharine Brem
- Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany
- Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- University Hospital of Old Age Psychiatry, University of Bern, Bern, Switzerland
- Department of Neuropsychology, Lucerne Psychiatry, Lucerne, Switzerland
| | - Philipp Sämann
- Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany
| | - Leonhard Schilbach
- Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany
- Independent Max Planck Research Group for Social Neuroscience, München, Germany
- Ludwig-Maximilians-Universität, Munich, Germany
| | - Martin E Keck
- Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany
- Schmieder Hospital in Gailingen, Gailingen, Germany
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86
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Genome-wide association study and polygenic risk score analysis of esketamine treatment response. Sci Rep 2020; 10:12649. [PMID: 32724131 PMCID: PMC7387452 DOI: 10.1038/s41598-020-69291-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 07/01/2020] [Indexed: 12/13/2022] Open
Abstract
To elucidate the genetic underpinnings of the antidepressant efficacy of S-ketamine (esketamine) nasal spray in major depressive disorder (MDD), we performed a genome-wide association study (GWAS) in cohorts of European ancestry (n = 527). This analysis was followed by a polygenic risk score approach to test for associations between genetic loading for psychiatric conditions, symptom profiles and esketamine efficacy. We identified a genome-wide significant locus in IRAK3 (p = 3.57 × 10–8, rs11465988, β = − 51.6, SE = 9.2) and a genome-wide significant gene-level association in NME7 (p = 1.73 × 10–6) for esketamine efficacy (i.e. percentage change in symptom severity score compared to baseline). Additionally, the strongest association with esketamine efficacy identified in the polygenic score analysis was from the genetic loading for depressive symptoms (p = 0.001, standardized coefficient β = − 3.1, SE = 0.9), which did not reach study-wide significance. Pathways relevant to neuronal and synaptic function, immune signaling, and glucocorticoid receptor/stress response showed enrichment among the suggestive GWAS signals.
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87
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Athreya AP, Iyer R, Wang L, Weinshilboum RM, Bobo WV. Integration of machine learning and pharmacogenomic biomarkers for predicting response to antidepressant treatment: can computational intelligence be used to augment clinical assessments? Pharmacogenomics 2020; 20:983-988. [PMID: 31559920 DOI: 10.2217/pgs-2019-0119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Affiliation(s)
- Arjun P Athreya
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Ravishankar Iyer
- Department of Electrical & Computer Engineering, University of Illinois at Urbana-Champaign, IL 61820, USA
| | - Liewei Wang
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Richard M Weinshilboum
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - William V Bobo
- Department of Psychiatry & Psychology, Mayo Clinic, Jacksonville, FL 32224, USA
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88
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Ermers NJ, Hagoort K, Scheepers FE. The Predictive Validity of Machine Learning Models in the Classification and Treatment of Major Depressive Disorder: State of the Art and Future Directions. Front Psychiatry 2020; 11:472. [PMID: 32523557 PMCID: PMC7261928 DOI: 10.3389/fpsyt.2020.00472] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 05/07/2020] [Indexed: 11/13/2022] Open
Abstract
Major depressive disorder imposes a substantial disease burden worldwide, ranking as the third leading contributor to global disability. In spite of its ubiquity, classifying and treating depression has proven troublesome. One argument put forward to explain this predicament is the heterogeneity of patients diagnosed with the disorder. Recently, many areas of daily life have witnessed the surge of machine learning techniques, computational approaches to elucidate complex patterns in large datasets, which can be employed to make predictions and detect relevant clusters. Due to the multidimensionality at play in the pathogenesis of depression, it is suggested that machine learning could contribute to improving classification and treatment. In this paper, we investigated literature focusing on the use of machine learning models on datasets with clinical variables of patients diagnosed with depression to predict treatment outcomes or find more homogeneous subgroups. Identified studies based on best practices in the field are evaluated. We found 16 studies predicting outcomes (such as remission) and identifying clusters in patients with depression. The identified studies are mostly still in proof-of-concept phase, with small datasets, lack of external validation, and providing single performance metrics. Larger datasets, and models with similar variables present across these datasets, are needed to develop accurate and generalizable models. We hypothesize that harnessing natural language processing to obtain data 'hidden' in clinical texts might prove useful in improving prediction models. Besides, researchers will need to focus on the conditions to feasibly implement these models to support psychiatrists and patients in their decision-making in practice. Only then we can enter the realm of precision psychiatry.
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Affiliation(s)
- Nick J. Ermers
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
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89
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Predicting treatment effects in unipolar depression: A meta-review. Pharmacol Ther 2020; 212:107557. [PMID: 32437828 DOI: 10.1016/j.pharmthera.2020.107557] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Accepted: 04/23/2020] [Indexed: 12/23/2022]
Abstract
There is increasing interest in clinical prediction models in psychiatry, which focus on developing multivariate algorithms to guide personalized diagnostic or management decisions. The main target of these models is the prediction of treatment response to different antidepressant therapies. This is because the ability to predict response based on patients' personal data may allow clinicians to make improved treatment decisions, and to provide more efficacious or more tolerable medications to the right patient. We searched the literature for systematic reviews about treatment prediction in the context of existing treatment modalities for adult unipolar depression, until July 2019. Treatment effect is defined broadly to include efficacy, safety, tolerability and acceptability outcomes. We first focused on the identification of individual predictor variables that might predict treatment response, and second, we considered multivariate clinical prediction models. Our meta-review included a total of 10 systematic reviews; seven (from 2014 to 2018) focusing on individual predictor variables and three focusing on clinical prediction models. These identified a number of sociodemographic, phenomenological, clinical, neuroimaging, remote monitoring, genetic and serum marker variables as possible predictor variables for treatment response, alongside statistical and machine-learning approaches to clinical prediction model development. Effect sizes for individual predictor variables were generally small and clinical prediction models had generally not been validated in external populations. There is a need for rigorous model validation in large external data-sets to prove the clinical utility of models. We also discuss potential future avenues in the field of personalized psychiatry, particularly the combination of multiple sources of data and the emerging field of artificial intelligence and digital mental health to identify new individual predictor variables.
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90
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Du X, Yin M, Yuan L, Zhang G, Fan Y, Li Z, Yuan N, Lv X, Zhao X, Zou S, Deng W, Kosten TR, Zhang XY. Reduction of depression-like behavior in rat model induced by ShRNA targeting norepinephrine transporter in locus coeruleus. Transl Psychiatry 2020; 10:130. [PMID: 32366842 PMCID: PMC7198598 DOI: 10.1038/s41398-020-0808-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 04/07/2020] [Accepted: 04/16/2020] [Indexed: 02/05/2023] Open
Abstract
Depression may be associated with reduced monoamine neurotransmission, particularly serotonin and norepinephrine (NE). Reuptake of NE by the norepinephrine transporter (NET) is the primary mechanism by which many of the antidepressants are high-affinity substrates for NET. This study aimed to examine the effect of lentivirus-mediated shRNA targeting NET in locus coeruleus (LC) on depression-like behaviors of rats. We randomly assigned 60 male Wistar rats to 6 experimental groups: (1) Control group: without chronic unpredictable mild stress (CUMS) and without NET-shRNA treatment; (2) shRNA group: without CUMS + NET-shRNA; (3) CUMS group: 3-week CUMS without NET-shRNA; (4) CUMS + nonsense shRNA group; (5) CUMS + amygdala (Amy)-shRNA group; (6) CUMS+ locus coeruleus (LC)-shRNA group. First, recombinant lentiviral vector expressing shRNA (ShRNA-629, ShRNA-330, ShRNA-1222, ShRNA-1146 or ShRNA- negative control) against NET were produced, and their efficiency in knocking down of NET in PC12 cells were assessed by Q-PCR and western blot analysis. Second, shRNA was injected into the rat LC bilaterally to investigate whether it could prevent the depressive-like behavior induced by 3-week CUMS. Third, we tested the depressive-like behavior of the rats in the forced swimming test, the open field test, the sucrose preference test, as well as the body weight gain at the end of the seventh week. Finally, the protein expressions of NET was measured by western blot and the NE levels were measured by high performance liquid chromatography. Q-PCR and western blot showed that the ShRNA-1146 had the best interference efficiency targeting on NET in PC12 cells (p < 0.01). Compared to the depression model group, the immobility time in the forced swimming test was significantly reduced (p < 0.01), but the sucrose preference and the total scores in the open field test were significantly increased (all p < 0.01) in the group treated with shRNA in LC. Furthermore, compared with the depression model group, NET levels were significantly decreased (p < 0.01), but NE levels were significantly increased in the group treated with shRNA in LC (p < 0.05). Our findings suggest that Lentivirus-mediated shRNA targeting NET in LC downregulated NET both in vitro and in vivo, resulting in a significant decrease in depressive-like behavior of rats.
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Affiliation(s)
- Xiangdong Du
- Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China.
| | - Ming Yin
- grid.263761.70000 0001 0198 0694Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Lian Yuan
- grid.263761.70000 0001 0198 0694Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Guangya Zhang
- grid.263761.70000 0001 0198 0694Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Yan Fan
- grid.263761.70000 0001 0198 0694Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Zhe Li
- grid.263761.70000 0001 0198 0694Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Nian Yuan
- grid.263761.70000 0001 0198 0694Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Xiaoli Lv
- grid.263761.70000 0001 0198 0694Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Xueli Zhao
- grid.263761.70000 0001 0198 0694Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Siyun Zou
- grid.263761.70000 0001 0198 0694Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Wei Deng
- grid.13291.380000 0001 0807 1581Department of Psychiatry and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Thomas R. Kosten
- grid.39382.330000 0001 2160 926XDepartment of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX USA
| | - Xiang Yang Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China. .,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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91
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Levchenko A, Nurgaliev T, Kanapin A, Samsonova A, Gainetdinov RR. Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders. Heliyon 2020; 6:e03990. [PMID: 32462093 PMCID: PMC7240336 DOI: 10.1016/j.heliyon.2020.e03990] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 10/31/2019] [Accepted: 05/12/2020] [Indexed: 12/13/2022] Open
Abstract
A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental illness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category. In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a next step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention to machine learning algorithms that can be used with the goal of handling multidimensional datasets.
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Affiliation(s)
- Anastasia Levchenko
- Theodosius Dobzhansky Center for Genome Bioinformatics, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Timur Nurgaliev
- Institute of Translational Biomedicine, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Alexander Kanapin
- Theodosius Dobzhansky Center for Genome Bioinformatics, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Anastasia Samsonova
- Theodosius Dobzhansky Center for Genome Bioinformatics, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Raul R. Gainetdinov
- Institute of Translational Biomedicine, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
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92
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McAllister-Williams RH, Arango C, Blier P, Demyttenaere K, Falkai P, Gorwood P, Hopwood M, Javed A, Kasper S, Malhi GS, Soares JC, Vieta E, Young AH, Papadopoulos A, Rush AJ. The identification, assessment and management of difficult-to-treat depression: An international consensus statement. J Affect Disord 2020; 267:264-282. [PMID: 32217227 DOI: 10.1016/j.jad.2020.02.023] [Citation(s) in RCA: 140] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 01/07/2020] [Accepted: 02/06/2020] [Indexed: 12/26/2022]
Abstract
BACKGROUND Many depressed patients are not able to achieve or sustain symptom remission despite serial treatment trials - often termed "treatment resistant depression". A broader, perhaps more empathic concept of "difficult-to-treat depression" (DTD) was considered. METHODS A consensus group discussed the definition, clinical recognition, assessment and management implications of the DTD heuristic. RESULTS The group proposed that DTD be defined as "depression that continues to cause significant burden despite usual treatment efforts". All depression management should include a thorough initial assessment. When DTD is recognized, a regular reassessment that employs a multi-dimensional framework to identify addressable barriers to successful treatment (including patient-, illness- and treatment-related factors) is advised, along with specific recommendations for addressing these factors. The emphasis of treatment, in the first instance, shifts from a goal of remission to optimal symptom control, daily psychosocial functional and quality of life, based on a patient-centred approach with shared decision-making to enhance the timely consideration of all treatment options (including pharmacotherapy, psychotherapy, neurostimulation, etc.) to optimize outcomes when sustained remission is elusive. LIMITATIONS The recommended definition and management of DTD is based largely on expert consensus. While DTD would seem to have clinical utility, its specificity and objectivity may be insufficient to define clinical populations for regulatory trial purposes, though DTD could define populations for service provision or phase 4 trials. CONCLUSIONS DTD provides a clinically useful conceptualization that implies a search for and remediation of specific patient-, illness- and treatment obstacles to optimizing outcomes of relevance to patients.
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Affiliation(s)
- R H McAllister-Williams
- Northern Centre for Mood Disorders, Newcastle University, UK; Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust, Newcastle upon Tyne, UK.
| | - C Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), CIBERSAM, School of Medicine, Universidad Complutense, Madrid, Spain
| | - P Blier
- Royal Ottawa Institute of Mental Health Research, University of Ottawa, Canada
| | - K Demyttenaere
- University Psychiatric Center KU Leuven, Faculty of Medicine KU Leuven, Belgium
| | - P Falkai
- Clinic for Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich, Germany
| | - P Gorwood
- CMME, Hopital Sainte-Anne (GHU Paris et Neurosciences). Paris-Descartes University, INSERM U1266, Paris, France
| | - M Hopwood
- University of Melbourne, Melbourne, Australia
| | - A Javed
- Faculty of the University of Warwick, UK
| | - S Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
| | - G S Malhi
- The University of Sydney, Faculty of Medicine and Health, Northern Clinical School, Department of Psychiatry, Sydney, New South Wales, Australia; Academic Department of Psychiatry, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW 2065 Australia; CADE Clinic, Royal North Shore Hospital, Northern Sydney Local Health District, St Leonards, NSW 2065 Australia
| | - J C Soares
- University of Texas Health Science Center, Houston, TX, USA
| | - E Vieta
- Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - A H Young
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London & South London and Maudsley NHS Foundation Trust, UK
| | | | - A J Rush
- Duke University School of Medicine, Durham, NC, USA; Texas Tech University Health Sciences Center, Permian Basin, Midland, TX, USA; Duke-NUS Medical School, Singapore
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93
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Perna G, Alciati A, Daccò S, Grassi M, Caldirola D. Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables. Psychiatry Investig 2020; 17:193-206. [PMID: 32160691 PMCID: PMC7113177 DOI: 10.30773/pi.2019.0289] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 01/14/2020] [Indexed: 02/06/2023] Open
Abstract
Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized psychiatry attempts to tailor therapeutic interventions according to each patient's unique profile and characteristics. This approach can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e., body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder. We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/ clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient with MDD.
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Affiliation(s)
- Giampaolo Perna
- Humanitas University Department of Biomedical Sciences, Milan, Italy.,Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, 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, USA
| | - Alessandra Alciati
- Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy.,Humanitas Clinical and Research Center, IRCCS, Milan, Italy
| | - Silvia Daccò
- Humanitas University Department of Biomedical Sciences, Milan, Italy.,Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy
| | - Massimiliano Grassi
- Humanitas University Department of Biomedical Sciences, Milan, Italy.,Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy
| | - Daniela Caldirola
- Humanitas University Department of Biomedical Sciences, Milan, Italy.,Department of Clinical Neurosciences, Villa San Benedetto Menni Hospital, Hermanas Hospitalarias, Como, Italy
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94
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Neuroimaging as a Tool for Individualized Treatment Choice in Depression: the Past, the Present and the Future. Curr Behav Neurosci Rep 2020. [DOI: 10.1007/s40473-020-00198-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Abstract
Purpose of Review
This paper aims to review the findings on neuroimaging as a tool for facilitating individualized treatment choice in depression.
Recent Findings
Neuroimaging has allowed the exploration of neural candidates for response biomarkers. In less than two decades, the field has expanded from small single drug studies to large multisite initiatives testing multiple interventions; from simple analytical methods to employing artificial intelligence, with an aim of establishing models based on a variety of data, such as neuroimaging, biological, psychological and clinical measures.
Summary
Neural biomarkers of response may play an important role in treatment response prediction. It seems likely that they will need to be considered together with other types of data in complex models in order to achieve the high accuracy and generalizability of results necessary for clinical use.
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95
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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.
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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.)
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96
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Mehltretter J, Rollins C, Benrimoh D, Fratila R, Perlman K, Israel S, Miresco M, Wakid M, Turecki G. Analysis of Features Selected by a Deep Learning Model for Differential Treatment Selection in Depression. Front Artif Intell 2020; 2:31. [PMID: 33733120 PMCID: PMC7861264 DOI: 10.3389/frai.2019.00031] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 12/06/2019] [Indexed: 12/13/2022] Open
Abstract
Background: Deep learning has utility in predicting differential antidepressant treatment response among patients with major depressive disorder, yet there remains a paucity of research describing how to interpret deep learning models in a clinically or etiologically meaningful way. In this paper, we describe methods for analyzing deep learning models of clinical and demographic psychiatric data, using our recent work on a deep learning model of STAR*D and CO-MED remission prediction. Methods: Our deep learning analysis with STAR*D and CO-MED yielded four models that predicted response to the four treatments used across the two datasets. Here, we use classical statistics and simple data representations to improve interpretability of the features output by our deep learning model and provide finer grained understanding of their clinical and etiological significance. Specifically, we use representations derived from our model to yield features predicting both treatment non-response and differential treatment response to four standard antidepressants, and use linear regression and t-tests to address questions about the contribution of trauma, education, and somatic symptoms to our models. Results: Traditional statistics were able to probe the input features of our deep learning models, reproducing results from previous research, while providing novel insights into depression causes and treatments. We found that specific features were predictive of treatment response, and were able to break these down by treatment and non-response categories; that specific trauma indices were differentially predictive of baseline depression severity; that somatic symptoms were significantly different between males and females, and that education and low income proved important psycho-social stressors associated with depression. Conclusion: Traditional statistics can augment interpretation of deep learning models. Such interpretation can lend us new hypotheses about depression and contribute to building causal models of etiology and prognosis. We discuss dataset-specific effects and ideal clinical samples for machine learning analysis aimed at improving tools to assist in optimizing treatment.
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Affiliation(s)
- Joseph Mehltretter
- Department of Computer Science, University of Southern California, Los Angeles, CA, United States
| | - Colleen Rollins
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, QC, Canada.,Faculty of Medicine, McGill University, Montreal, QC, Canada.,Douglas Mental Health University Institute, Montreal, QC, Canada.,Aifred Health, Montreal, QC, Canada
| | | | - Kelly Perlman
- Douglas Mental Health University Institute, Montreal, QC, Canada.,Aifred Health, Montreal, QC, Canada
| | - Sonia Israel
- Douglas Mental Health University Institute, Montreal, QC, Canada.,Aifred Health, Montreal, QC, Canada
| | - Marc Miresco
- Aifred Health, Montreal, QC, Canada.,Department of Psychiatry, Jewish General Hospital, Montreal, QC, Canada
| | - Marina Wakid
- Douglas Mental Health University Institute, Montreal, QC, Canada
| | - Gustavo Turecki
- Department of Psychiatry, McGill University, Montreal, QC, Canada.,Douglas Mental Health University Institute, Montreal, QC, Canada
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97
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Mehltretter J, Fratila R, Benrimoh DA, Kapelner A, Perlman K, Snook E, Israel S, Armstrong C, Miresco M, Turecki G. Differential Treatment Benet Prediction for Treatment Selection in
Depression: A Deep Learning Analysis of STAR*D and CO-MED Data. COMPUTATIONAL PSYCHIATRY 2020. [DOI: 10.1162/cpsy_a_00029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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98
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Agorastos A, Sommer A, Heinig A, Wiedemann K, Demiralay C. Vasopressin Surrogate Marker Copeptin as a Potential Novel Endocrine Biomarker for Antidepressant Treatment Response in Major Depression: A Pilot Study. Front Psychiatry 2020; 11:453. [PMID: 32508691 PMCID: PMC7251160 DOI: 10.3389/fpsyt.2020.00453] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 05/05/2020] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Major depressive disorder (MDD) constitutes the leading cause of disability worldwide. Although efficacious antidepressant pharmacotherapies exist for MDD, only about 40-60% of the patients respond to initial treatment. However, there is still a lack of robustly established and applicable biomarkers for antidepressant response in everyday clinical practice. OBJECTIVE This study targets the assessment of the vasopressin (AVP) surrogate marker Copeptin (CoP), as a potential peripheral hypothalamic-level biomarker of antidepressant treatment response in MDD. METHODS We measured baseline and dynamic levels of plasma CoP along with plasma ACTH and cortisol (CORT) in drug-naive outpatients with MDD before and after overnight manipulation of the hypothalamic-pituitary-adrenal (HPA) axis [i.e., stimulation (metyrapone) and suppression (dexamethasone)] on three consecutive days and their association with treatment response to 4 weeks of escitalopram treatment. RESULTS Our findings suggest significantly higher baseline and post-metyrapone plasma CoP levels in future non-responders, a statistically significant invert association between baseline CoP levels and probability of treatment response and a potential baseline plasma CoP cut-off level of above 2.9 pmol/L for future non-response screening. Baseline and dynamic plasma ACTH and CORT levels showed no association with treatment response. CONCLUSIONS This pilot study provide first evidence in humans that CoP may represent a novel, clinically easily applicable, endocrine biomarker of antidepressant response, based on a single-measurement, cut-off level. These findings, underline the role of the vasopressinergic system in the pathophysiology of MDD and may represent a significant new tool in the clinical and biological phenotyping of MDD enhancing individual-tailored therapies.
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Affiliation(s)
- Agorastos Agorastos
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Department of Psychiatry, Division of Neurosciences, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.,VA Center of Excellence for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, CA, United States
| | - Anne Sommer
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexandra Heinig
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Klaus Wiedemann
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Cüneyt Demiralay
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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99
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Plöderl M, Hengartner MP. What are the chances for personalised treatment with antidepressants? Detection of patient-by-treatment interaction with a variance ratio meta-analysis. BMJ Open 2019; 9:e034816. [PMID: 31874900 PMCID: PMC7008413 DOI: 10.1136/bmjopen-2019-034816] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVES To investigate if the treatment effect of antidepressants in patients with depression substantially varies in each patient (patient-by-treatment interaction or treatment heterogeneity), a necessary but largely unexplored prerequisite of personalised antidepressant treatment. DESIGN Meta-analytic variance comparison of treatment outcome between drug arms and placebo arms of clinical trials, based on the assumption that patient-by-treatment interaction should lead to larger variances in drug arms than placebo arms. To put the results into context, we run simple simulations, assuming different definitions and rates of those who respond especially well to antidepressants. DATA SOURCES 163 randomised, placebo-controlled trials (51 396 patients) with complete results for pre-post differences, selected from a recently published systematic review. ANALYSIS Variance ratios (VRs) and coefficients of variance ratios (CVRs) of individual trials were meta-analytically combined. The analysis was repeated for classes of antidepressants and specific antidepressants. RESULTS VRs (VR=1.01, CI 0.99 to 1.02) and CVRs (CVR=0.82, CI 0.80 to 0.84) of the antidepressant-treatment arms were comparable or smaller than in placebo arms. Similar results were observed for classes of antidepressants and for specific antidepressants. Our simulation analysis confirmed that equal VRs can only be obtained if they are not more than a few patients who respond slightly above average. CONCLUSIONS The lack of increased treatment-outcome variance in the antidepressants versus placebo groups in randomised controlled trials indicates that no or only very small subgroups of patients respond particularly well to antidepressants. Thus, the scope for personalised treatment with antidepressants seems to be limited.
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Affiliation(s)
- Martin Plöderl
- Department of Clinical Psychology, University Clinic for Psychiatry, Psychotherapy, and Psychosomatics, Salzburg, Austria
- Department of Crisis Intervention and Suicide Prevention, Christian Doppler Clinic, Paracelsus Medical University, Salzburg, Austria
| | - Michael Pascal Hengartner
- Section for Clinical Psychology and Health Psychology, Zurich University of Applied Sciences/ZHAW, Zurich, Switzerland
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100
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Taylor RW, Marwood L, Greer B, Strawbridge R, Cleare AJ. Predictors of response to augmentation treatment in patients with treatment-resistant depression: A systematic review. J Psychopharmacol 2019; 33:1323-1339. [PMID: 31526204 DOI: 10.1177/0269881119872194] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Treatment-resistant depression is an important contributor to the global burden of depression. Antidepressant augmentation is a recommended treatment strategy for treatment-resistant patients, but outcomes remain poor. Identifying factors that are predictive of response to augmentation treatments may improve outcomes. AIMS This review aimed to synthesise the existing literature examining predictors of response to augmentation treatments in patients who had insufficiently responded to initial treatment. METHODS A systematic search was conducted identifying 2241 unique manuscripts. 24 examining predictors of outcome to pharmacological or psychological augmentation treatment were included in this review. RESULTS Atypical antipsychotics were the most frequently assessed treatment class (nine studies), closely followed by mood stabilisers (eight studies). Only one eligible psychological augmentation study was identified. Early response to treatment (week 2) was the best-supported predictor of subsequent treatment outcome, reported by six studies. Many predictor variables were only assessed by one report and others such as pre-treatment severity yielded contradictory results, both within and across treatment classes. CONCLUSIONS This review highlights the importance of early response as a predictor of pharmacological augmentation outcome, with implications for both the monitoring and treatment of resistant unipolar patients. Further replication is needed across specific interventions to fully assess the generalisability of this finding. However, the clear lack of consistent evidence for other predictive factors both within and across treatments, and the scarce examination of psychological augmentation, demonstrates the need for much more research of a high quality if response prediction is to improve outcomes for patients with treatment-resistant depression.
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Affiliation(s)
- Rachael W Taylor
- The Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- The National Institute for Health Research Maudsley Biomedical Research Centre, South London & Maudsley NHS Foundation Trust, London, UK
| | - Lindsey Marwood
- The Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Ben Greer
- The National Institute for Health Research Maudsley Biomedical Research Centre, South London & Maudsley NHS Foundation Trust, London, UK
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Rebecca Strawbridge
- The Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- The National Institute for Health Research Maudsley Biomedical Research Centre, South London & Maudsley NHS Foundation Trust, London, UK
| | - Anthony J Cleare
- The Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- The National Institute for Health Research Maudsley Biomedical Research Centre, South London & Maudsley NHS Foundation Trust, London, UK
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