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Perlman K, Mehltretter J, Benrimoh D, Armstrong C, Fratila R, Popescu C, Tunteng JF, Williams J, Rollins C, Golden G, Turecki G. Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets. Transl Psychiatry 2024; 14:263. [PMID: 38906883 PMCID: PMC11192904 DOI: 10.1038/s41398-024-02970-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 05/09/2024] [Accepted: 05/29/2024] [Indexed: 06/23/2024] Open
Abstract
Major depressive disorder (MDD) is the leading cause of disability worldwide, yet treatment selection still proceeds via "trial and error". Given the varied presentation of MDD and heterogeneity of treatment response, the use of machine learning to understand complex, non-linear relationships in data may be key for treatment personalization. Well-organized, structured data from clinical trials with standardized outcome measures is useful for training machine learning models; however, combining data across trials poses numerous challenges. There is also persistent concern that machine learning models can propagate harmful biases. We have created a methodology for organizing and preprocessing depression clinical trial data such that transformed variables harmonized across disparate datasets can be used as input for feature selection. Using Bayesian optimization, we identified an optimal multi-layer dense neural network that used data from 21 clinical and sociodemographic features as input in order to perform differential treatment benefit prediction. With this combined dataset of 5032 individuals and 6 drugs, we created a differential treatment benefit prediction model. Our model generalized well to the held-out test set and produced similar accuracy metrics in the test and validation set with an AUC of 0.7 when predicting binary remission. To address the potential for bias propagation, we used a bias testing performance metric to evaluate the model for harmful biases related to ethnicity, age, or sex. We present a full pipeline from data preprocessing to model validation that was employed to create the first differential treatment benefit prediction model for MDD containing 6 treatment options.
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Affiliation(s)
- Kelly Perlman
- Douglas Mental Health University Institute, Montreal, QC, Canada.
- McGill University, Montreal, QC, Canada.
- Aifred Health Inc., Montreal, QC, Canada.
| | | | - David Benrimoh
- Douglas Mental Health University Institute, Montreal, QC, Canada
- McGill University, Montreal, QC, Canada
- Aifred Health Inc., Montreal, QC, Canada
| | | | | | - Christina Popescu
- Aifred Health Inc., Montreal, QC, Canada
- University of Alberta, Edmonton, AB, Canada
| | - Jingla-Fri Tunteng
- McGill University, Montreal, QC, Canada
- Aifred Health Inc., Montreal, QC, Canada
| | - Jerome Williams
- McGill University, Montreal, QC, Canada
- Aifred Health Inc., Montreal, QC, Canada
| | - Colleen Rollins
- McGill University, Montreal, QC, Canada
- University of Cambridge, Cambridge, UK
| | - Grace Golden
- Aifred Health Inc., Montreal, QC, Canada
- University of Waterloo, Waterloo, ON, Canada
| | - Gustavo Turecki
- Douglas Mental Health University Institute, Montreal, QC, Canada
- McGill University, Montreal, QC, Canada
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Brouwer JMJL, Wardenaar KJ, Nolte IM, Liemburg EJ, Bet PM, Snieder H, Mulder H, Cath DC, Penninx BWJH. Association of CYP2D6 and CYP2C19 metabolizer status with switching and discontinuing antidepressant drugs: an exploratory study. BMC Psychiatry 2024; 24:394. [PMID: 38797832 PMCID: PMC11129450 DOI: 10.1186/s12888-024-05764-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 04/15/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Tailoring antidepressant drugs (AD) to patients' genetic drug-metabolism profile is promising. However, literature regarding associations of ADs' treatment effect and/or side effects with drug metabolizing genes CYP2D6 and CYP2C19 has yielded inconsistent results. Therefore, our aim was to longitudinally investigate associations between CYP2D6 (poor, intermediate, and normal) and CYP2C19 (poor, intermediate, normal, and ultrarapid) metabolizer-status, and switching/discontinuing of ADs. Next, we investigated whether the number of perceived side effects differed between metabolizer statuses. METHODS Data came from the multi-site naturalistic longitudinal cohort Netherlands Study of Depression and Anxiety (NESDA). We selected depression- and/or anxiety patients, who used AD at some point in the course of the 9 years follow-up period (n = 928). Medication use was followed to assess patterns of AD switching/discontinuation over time. CYP2D6 and CYP2C19 alleles were derived using genome-wide data of the NESDA samples and haplotype data from the PharmGKB database. Logistic regression analyses were conducted to investigate the association of metabolizer status with switching/discontinuing ADs. Mann-Whitney U-tests were conducted to compare the number of patient-perceived side effects between metabolizer statuses. RESULTS No significant associations were observed of CYP metabolizer status with switching/discontinuing ADs, nor with the number of perceived side effects. CONCLUSIONS We found no evidence for associations between CYP metabolizer statuses and switching/discontinuing AD, nor with side effects of ADs, suggesting that metabolizer status only plays a limited role in switching/discontinuing ADs. Additional studies with larger numbers of PM and UM patients are needed to further determine the potential added value of pharmacogenetics to guide pharmacotherapy.
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Affiliation(s)
- Jurriaan M J L Brouwer
- Research School of Behavioral and Cognitive Neurosciences, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
- GGZ Drenthe Mental Health Center Drenthe, Assen, The Netherlands.
- Department of Clinical Pharmacy, Wilhelmina Hospital Assen, Assen, The Netherlands.
- Department of Clinical Pharmacy, Martini Hospital Groningen, Van Swietenlaan 1, Groningen, 9728 NT, The Netherlands.
| | - Klaas J Wardenaar
- GGZ Drenthe Mental Health Center Drenthe, Assen, The Netherlands
- Department of Psychiatry, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, Groningen, The Netherlands
- Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Edith J Liemburg
- GGZ Drenthe Mental Health Center Drenthe, Assen, The Netherlands
- Rob Giel Research Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Pierre M Bet
- Department of Clinical Pharmacology and Pharmacy, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Harold Snieder
- Rob Giel Research Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Hans Mulder
- Department of Clinical Pharmacy, Wilhelmina Hospital Assen, Assen, The Netherlands
| | - Danielle C Cath
- Research School of Behavioral and Cognitive Neurosciences, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- GGZ Drenthe Mental Health Center Drenthe, Assen, The Netherlands
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam Public Health, Amsterdam, The Netherlands
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Simon GE, Cruz M, Boggs JM, Beck A, Shortreed SM, Coley RY. Predicting Outcomes of Antidepressant Treatment in Community Practice Settings. Psychiatr Serv 2024; 75:419-426. [PMID: 38050444 DOI: 10.1176/appi.ps.20230380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
OBJECTIVE The authors examined whether machine-learning models could be used to analyze data from electronic health records (EHRs) to predict patients' responses to antidepressant medications. METHODS EHR data from a Washington State health system identified patients ages ≥13 years who started an antidepressant medication in 2016 in a community practice setting and had a baseline Patient Health Questionnaire-9 (PHQ-9) score of ≥10 and at least one PHQ-9 score recorded 14-180 days later. Potential predictors of a response to antidepressants were extracted from the EHR and included demographic characteristics, psychiatric and substance use diagnoses, past psychiatric medication use, mental health service use, and past PHQ-9 scores. Random-forest and penalized regression analyses were used to build models predicting follow-up PHQ-9 score and a favorable treatment response (≥50% improvement in score). RESULTS Among 2,469 patients starting antidepressant medication treatment, the mean±SD baseline PHQ-9 score was 17.3±4.5, and the mean lowest follow-up score was 9.2±5.9. Outcome data were available for 72% of the patients. About 48% of the patients had a favorable treatment response. The best-fitting random-forest models yielded a correlation between predicted and observed follow-up scores of 0.38 (95% CI=0.32-0.45) and an area under the receiver operating characteristic curve for a favorable response of 0.57 (95% CI=0.52-0.61). Results were similar for penalized regression models and for models predicting last PHQ-9 score during follow-up. CONCLUSIONS Prediction models using EHR data were not accurate enough to inform recommendations for or against starting antidepressant medication. Personalization of depression treatment should instead rely on systematic assessment of early outcomes.
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Affiliation(s)
- Gregory E Simon
- Kaiser Permanente Washington Health Research Institute, Seattle (Simon, Cruz, Shortreed, Coley); Kaiser Permanente Colorado Institute for Health Research, Aurora (Boggs, Beck)
| | - Maricela Cruz
- Kaiser Permanente Washington Health Research Institute, Seattle (Simon, Cruz, Shortreed, Coley); Kaiser Permanente Colorado Institute for Health Research, Aurora (Boggs, Beck)
| | - Jennifer M Boggs
- Kaiser Permanente Washington Health Research Institute, Seattle (Simon, Cruz, Shortreed, Coley); Kaiser Permanente Colorado Institute for Health Research, Aurora (Boggs, Beck)
| | - Arne Beck
- Kaiser Permanente Washington Health Research Institute, Seattle (Simon, Cruz, Shortreed, Coley); Kaiser Permanente Colorado Institute for Health Research, Aurora (Boggs, Beck)
| | - Susan M Shortreed
- Kaiser Permanente Washington Health Research Institute, Seattle (Simon, Cruz, Shortreed, Coley); Kaiser Permanente Colorado Institute for Health Research, Aurora (Boggs, Beck)
| | - R Yates Coley
- Kaiser Permanente Washington Health Research Institute, Seattle (Simon, Cruz, Shortreed, Coley); Kaiser Permanente Colorado Institute for Health Research, Aurora (Boggs, Beck)
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Eszlari N, Hullam G, Gal Z, Torok D, Nagy T, Millinghoffer A, Baksa D, Gonda X, Antal P, Bagdy G, Juhasz G. Olfactory genes affect major depression in highly educated, emotionally stable, lean women: a bridge between animal models and precision medicine. Transl Psychiatry 2024; 14:182. [PMID: 38589364 PMCID: PMC11002013 DOI: 10.1038/s41398-024-02867-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 03/06/2024] [Accepted: 03/11/2024] [Indexed: 04/10/2024] Open
Abstract
Most current approaches to establish subgroups of depressed patients for precision medicine aim to rely on biomarkers that require highly specialized assessment. Our present aim was to stratify participants of the UK Biobank cohort based on three readily measurable common independent risk factors, and to investigate depression genomics in each group to discover common and separate biological etiology. Two-step cluster analysis was run separately in males (n = 149,879) and females (n = 174,572), with neuroticism (a tendency to experience negative emotions), body fat percentage, and years spent in education as input variables. Genome-wide association analyses were implemented within each of the resulting clusters, for the lifetime occurrence of either a depressive episode or recurrent depressive disorder as the outcome. Variant-based, gene-based, gene set-based, and tissue-specific gene expression test were applied. Phenotypically distinct clusters with high genetic intercorrelations in depression genomics were found. A two-cluster solution was the best model in each sex with some differences including the less important role of neuroticism in males. In females, in case of a protective pattern of low neuroticism, low body fat percentage, and high level of education, depression was associated with pathways related to olfactory function. While also in females but in a risk pattern of high neuroticism, high body fat percentage, and less years spent in education, depression showed association with complement system genes. Our results, on one hand, indicate that alteration of olfactory pathways, that can be paralleled to the well-known rodent depression models of olfactory bulbectomy, might be a novel target towards precision psychiatry in females with less other risk factors for depression. On the other hand, our results in multi-risk females may provide a special case of immunometabolic depression.
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Grants
- This study was supported by the Hungarian National Research, Development, and Innovation Office, with grants K 143391 and PD 146014, as well as 2019-2.1.7-ERA-NET-2020-00005 under the frame of ERA PerMed (ERAPERMED2019-108); by the Hungarian Brain Research Program (grant: 2017-1.2.1-NKP-2017-00002) and the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022); and by TKP2021-EGA-25, implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-EGA funding scheme. N. E. was supported by the ÚNKP-22-4-II-SE-1, and D. B. by the ÚNKP-22-4-I-SE-10 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. N. E. is supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.
- This study was supported by the Hungarian National Research, Development, and Innovation Office, with grants K 143391, as well as 2019-2.1.7-ERA-NET-2020-00005 under the frame of ERA PerMed (ERAPERMED2019-108); by the Hungarian Brain Research Program (grant: 2017-1.2.1-NKP-2017-00002) and the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022); and by TKP2021-EGA-25, implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-EGA funding scheme.
- This study was supported by the Hungarian National Research, Development, and Innovation Office, with grants K 143391, as well as 2019-2.1.7-ERA-NET-2020-00005 under the frame of ERA PerMed (ERAPERMED2019-108); by the Hungarian Brain Research Program (grant: 2017-1.2.1-NKP-2017-00002) and the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022); and by TKP2021-EGA-25, implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-EGA funding scheme. N. E. was supported by the ÚNKP-22-4-II-SE-1, and D. B. by the ÚNKP-23-4-II-SE-2 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.
- This study was supported by the Hungarian National Research, Development, and Innovation Office, with grants K 139330, K 143391, and PD 146014, as well as 2019-2.1.7-ERA-NET-2020-00005 under the frame of ERA PerMed (ERAPERMED2019-108); by the Hungarian Brain Research Program (grant: 2017-1.2.1-NKP-2017-00002) and the Hungarian Brain Research Program 3.0 (NAP2022-I-4/2022); and by TKP2021-EGA-25, implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-EGA funding scheme. It was also supported by the National Research, Development, and Innovation Fund of Hungary under Grant TKP2021-EGA-02 and the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory.
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Affiliation(s)
- Nora Eszlari
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary.
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary.
| | - Gabor Hullam
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Zsofia Gal
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Dora Torok
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Tamas Nagy
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Andras Millinghoffer
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Daniel Baksa
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
- Department of Personality and Clinical Psychology, Institute of Psychology, Faculty of Humanities and Social Sciences, Pazmany Peter Catholic University, Budapest, Hungary
| | - Xenia Gonda
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Peter Antal
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Gyorgy Bagdy
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Gabriella Juhasz
- Department of Pharmacodynamics, Faculty of Pharmaceutical Sciences, Semmelweis University, Budapest, Hungary
- NAP3.0-SE Neuropsychopharmacology Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
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Kim KM, Lee KH, Kim H, Kim O, Kim JW. Symptom clusters in adolescent depression and differential responses of clusters to pharmacologic treatment. J Psychiatr Res 2024; 172:59-65. [PMID: 38364553 DOI: 10.1016/j.jpsychires.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 11/20/2023] [Accepted: 02/01/2024] [Indexed: 02/18/2024]
Abstract
OBJECTIVE Symptoms of depression in adolescents are widely variable, but they are often interactive and clustered. The analysis of interactions and clusters among individual symptoms may help predict treatment outcomes. We aimed to determine clusters of individual symptoms in adolescent depression and their changes in the response to pharmacological treatment. METHOD A total of 95 adolescents, aged 12-17 years, with major depressive disorder were included. Participants were treated with escitalopram, and depressive symptoms were assessed at baseline (V1) and 1, 2, 4, 6, and 8 weeks (V6). The severity of depression was assessed using the Children's Depression Rating Scale-Revised. To construct network and clustering structures among symptoms, the Gaussian graphical model and Exploratory Graph Analysis with the tuning parameter to minimize the extended Bayesian information criterion were adopted. RESULTS Exploratory Graph Analysis revealed that symptoms of depression comprised four clusters: impaired activity, somatic concerns, subjective mood, and observed affect. The main effect of visit with decreased symptom severity was significant in all four clusters; however, the degree of symptom improvement differed among the four clusters. The effect size of score differences from V1 to V6 was the highest in the subjective mood (Cohen's d = 1.075), and lowest in impaired activity (d = 0.501) clusters. CONCLUSION The present study identified four symptom clusters associated with adolescent depression and their differential changes related to antidepressant treatment. This finding suggests that escitalopram was the most effective at improving subjective mood among different clusters. However, other therapeutic modalities may be needed to improve other clusters of symptoms, consequently leading to increased overall improvement of depression in adolescents.
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Affiliation(s)
- Kyoung Min Kim
- Department of Psychiatry, College of Medicine, Dankook University, Cheonan, Republic of Korea; Department of Psychiatry, Dankook University Hospital, Cheonan, Republic of Korea
| | - Kyung Hwa Lee
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Haebin Kim
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ok Kim
- Department of Psychology, Graduate School of Dankook University, Cheonan, Republic of Korea
| | - Jae-Won Kim
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea; Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
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Honkalampi K, Kraav SL, Kerr P, Juster RP, Virtanen M, Hintsa T, Partonen T, Lehto SM. Associations of allostatic load with sociodemographic factors, depressive symptoms, lifestyle, and health characteristics in a large general population-based sample. J Affect Disord 2024; 350:784-791. [PMID: 38266933 DOI: 10.1016/j.jad.2024.01.189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 01/14/2024] [Accepted: 01/18/2024] [Indexed: 01/26/2024]
Abstract
OBJECTIVE We examined the associations between allostatic load (AL) and sociodemographic factors, depressive symptoms, lifestyle and health characteristics in a population-based sample of 4993 adults in Finland. METHODS Thirteen biomarkers were used to construct AL. High AL was defined as scoring highly in ≥4 items. RESULTS AL scores of 4 and above were exceeded in the age group of 45-54 years in men and 65-74 years in women. Age was the strongest predictor for belonging to the high AL score group. In addition, elevated depressive symptoms (BDI-6 ≥ 4), male sex, not engaging in physical exercise, high alcohol use and a low level of education were associated with an increased likelihood of belonging to the high AL group. CONCLUSION The older the participants were, the greater their AL burden was. However, AL burden increased more steeply as a function of age in men. In addition to lifestyle interventions, effective prevention strategies for depression at the population level could have a major public health impact in reducing the accumulation of AL burden.
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Affiliation(s)
- Kirsi Honkalampi
- School of Educational Sciences and Psychology, University of Eastern Finland, Joensuu, Finland.
| | - Siiri-Liisi Kraav
- Department of Social Sciences, University of Eastern Finland, Kuopio, Finland
| | - Philippe Kerr
- Department of Psychiatry and Addictology, Université de Montréal, Montréal, Canada
| | - Robert-Paul Juster
- Department of Psychiatry and Addictology, Université de Montréal, Montréal, Canada
| | - Marianna Virtanen
- School of Educational Sciences and Psychology, University of Eastern Finland, Joensuu, Finland
| | - Taina Hintsa
- School of Educational Sciences and Psychology, University of Eastern Finland, Joensuu, Finland
| | - Timo Partonen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Soili M Lehto
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway; R&D Department, Division of Mental Health Services, Akershus University Hospital, Lørenskog, Norway; Department of Psychiatry, University of Helsinki, Helsinki, Finland
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Banerjee S, Wu Y, Bingham KS, Marino P, Meyers BS, Mulsant BH, Neufeld NH, Oliver LD, Power JD, Rothschild AJ, Sirey JA, Voineskos AN, Whyte EM, Alexopoulos GS, Flint AJ. Trajectories of remitted psychotic depression: identification of predictors of worsening by machine learning. Psychol Med 2024; 54:1142-1151. [PMID: 37818656 DOI: 10.1017/s0033291723002945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
BACKGROUND Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory. METHOD One hundred and twenty-six persons aged 18-85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine. Latent class mixed modeling was used to identify subgroups of participants with distinct trajectories of depression severity during the RCT. Machine learning was used to predict membership to the trajectories based on participant pre-trajectory characteristics. RESULTS Seventy-one (56.3%) participants belonged to a subgroup with a stable trajectory of depression scores and 55 (43.7%) belonged to a subgroup with a worsening trajectory. A random forest model with high prediction accuracy (AUC of 0.812) found that the strongest predictors of membership to the worsening subgroup were residual depression symptoms at onset of remission, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode. In a logistic regression model that examined depression score at onset of remission as the only predictor variable, the AUC (0.778) was close to that of the machine learning model. CONCLUSIONS Residual depression at onset of remission has high accuracy in predicting membership to worsening outcome of remitted MDDPsy. Research is needed to determine how best to optimize the outcome of psychotic MDDPsy with residual symptoms.
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Affiliation(s)
- Samprit Banerjee
- Department of Population Health Sciences, Weill Cornell Medicine, New York, USA
| | - Yiyuan Wu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, USA
| | - Kathleen S Bingham
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
- Centre for Mental Health, University Health Network, Toronto, Canada
| | - Patricia Marino
- Department of Psychiatry, Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, New York, USA
| | - Barnett S Meyers
- Department of Psychiatry, Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, New York, USA
| | - Benoit H Mulsant
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
| | - Nicholas H Neufeld
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
| | | | | | - Anthony J Rothschild
- University of Massachusetts Chan Medical School and UMass Memorial Health Care, Worcester, USA
| | - Jo Anne Sirey
- Department of Psychiatry, Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, New York, USA
| | - Aristotle N Voineskos
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Addiction and Mental Health, Toronto, Canada
| | - Ellen M Whyte
- Department of Psychiatry, University of Pittsburgh School of Medicine and UPMC Western Psychiatric Hospital, Pittsburgh, USA
| | - George S Alexopoulos
- Department of Psychiatry, Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, New York, USA
| | - Alastair J Flint
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Centre for Mental Health, University Health Network, Toronto, Canada
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Wang J, Wu DD, DeLorenzo C, Yang J. Examining factors related to low performance of predicting remission in participants with major depressive disorder using neuroimaging data and other clinical features. PLoS One 2024; 19:e0299625. [PMID: 38547128 PMCID: PMC10977765 DOI: 10.1371/journal.pone.0299625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/13/2024] [Indexed: 04/02/2024] Open
Abstract
Major depressive disorder (MDD), a prevalent mental health issue, affects more than 8% of the US population, and almost 17% in the young group of 18-25 years old. Since Covid-19, its prevalence has become even more significant. However, the remission (being free of depression) rates of first-line antidepressant treatments on MDD are only about 30%. To improve treatment outcomes, researchers have built various predictive models for treatment responses and yet none of them have been adopted in clinical use. One reason is that most predictive models are based on data from subjective questionnaires, which are less reliable. Neuroimaging data are promising objective prognostic factors, but they are expensive to obtain and hence predictive models using neuroimaging data are limited and such studies were usually in small scale (N<100). In this paper, we proposed an advanced machine learning (ML) pipeline for small training dataset with large number of features. We implemented multiple imputation for missing data and repeated K-fold cross validation (CV) to robustly estimate predictive performances. Different feature selection methods and stacking methods using 6 general ML models including random forest, gradient boosting decision tree, XGBoost, penalized logistic regression, support vector machine (SVM), and neural network were examined to evaluate the model performances. All predictive models were compared using model performance metrics such as accuracy, balanced accuracy, area under ROC curve (AUC), sensitivity and specificity. Our proposed ML pipeline was applied to a training dataset and obtained an accuracy and AUC above 0.80. But such high performance failed while applying our ML pipeline using an external validation dataset from the EMBARC study which is a multi-center study. We further examined the possible reasons especially the site heterogeneity issue.
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Affiliation(s)
- Junying Wang
- Department of Applied Mathematics and Statistics, Stony Brook University, New York, New York, United states of America
| | - David D. Wu
- School of Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Christine DeLorenzo
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, New York, United States of America
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, United States of America
| | - Jie Yang
- Department of Family, Population & Preventive Medicine, Stony Brook University, Stony Brook, New York, United States of America
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9
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Curtiss J, Smoller JW, Pedrelli P. Optimizing precision medicine for second-step depression treatment: a machine learning approach. Psychol Med 2024:1-8. [PMID: 38533794 DOI: 10.1017/s0033291724000497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
BACKGROUND Less than a third of patients with depression achieve successful remission with standard first-step antidepressant monotherapy. The process for determining appropriate second-step care is often based on clinical intuition and involves a protracted course of trial and error, resulting in substantial patient burden and unnecessary delay in the provision of optimal treatment. To address this problem, we adopt an ensemble machine learning approach to improve prediction accuracy of remission in response to second-step treatments. METHOD Data were derived from the Level 2 stage of the STAR*D dataset, which included 1439 patients who were randomized into one of seven different second-step treatment strategies after failing to achieve remission during first-step antidepressant treatment. Ensemble machine learning models, comprising several individual algorithms, were evaluated using nested cross-validation on 155 predictor variables including clinical and demographic measures. RESULTS The ensemble machine learning algorithms exhibited differential classification performance in predicting remission status across the seven second-step treatments. For the full set of predictors, AUC values ranged from 0.51 to 0.82 depending on the second-step treatment type. Predicting remission was most successful for cognitive therapy (AUC = 0.82) and least successful for other medication and combined treatment options (AUCs = 0.51-0.66). CONCLUSION Ensemble machine learning has potential to predict second-step treatment. In this study, predictive performance varied by type of treatment, with greater accuracy in predicting remission in response to behavioral treatments than to pharmacotherapy interventions. Future directions include considering more informative predictor modalities to enhance prediction of second-step treatment response.
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Affiliation(s)
- Joshua Curtiss
- Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Jordan W Smoller
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Paola Pedrelli
- Depression Clinical and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
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10
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Tassone VK, Gholamali Nezhad F, Demchenko I, Rueda A, Bhat V. Amygdala biomarkers of treatment response in major depressive disorder: An fMRI systematic review of SSRI antidepressants. Psychiatry Res Neuroimaging 2024; 338:111777. [PMID: 38183847 DOI: 10.1016/j.pscychresns.2023.111777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/04/2023] [Accepted: 12/28/2023] [Indexed: 01/08/2024]
Abstract
Functional neuroimaging studies have demonstrated abnormal activity and functional connectivity (FC) of the amygdala among individuals with major depressive disorder (MDD), which may be rectified with selective serotonin reuptake inhibitor (SSRI) treatment. This systematic review aimed to identify changes in the amygdala on functional magnetic resonance imaging (fMRI) scans among individuals with MDD who received SSRIs. A search for fMRI studies examining amygdala correlates of SSRI response via fMRI was conducted through OVID (MEDLINE, PsycINFO, and Embase). The end date was April 4th, 2023. In total, 623 records were screened, and 16 studies were included in this review. While the search pertained to SSRIs broadly, the included studies were escitalopram-, citalopram-, fluoxetine-, sertraline-, and paroxetine-specific. Decreases in event-related amygdala activity were found following 6-to-12-week SSRI treatment, particularly in response to negative stimuli. Eight-week courses of SSRI pharmacotherapy were associated with increased event-related amygdala FC (i.e., with the prefrontal [PFC] and anterior cingulate cortices, insula, thalamus, caudate nucleus, and putamen) and decreased resting-state effective connectivity (i.e., amygdala-PFC). Preliminary evidence suggests that SSRIs may alter amygdala activity and FC in MDD. Additional studies are needed to corroborate findings. Future research should employ long-term follow-ups to determine whether effects persist after treatment termination.
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Affiliation(s)
- Vanessa K Tassone
- Interventional Psychiatry Program, St. Michael's Hospital, 193 Yonge Street 6-013, Toronto, Ontario M5B 1M8, Canada; Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, 1 King's College Circle, Medical Sciences Building, Toronto, Ontario M5S 1A8, Canada
| | - Fatemeh Gholamali Nezhad
- Interventional Psychiatry Program, St. Michael's Hospital, 193 Yonge Street 6-013, Toronto, Ontario M5B 1M8, Canada
| | - Ilya Demchenko
- Interventional Psychiatry Program, St. Michael's Hospital, 193 Yonge Street 6-013, Toronto, Ontario M5B 1M8, Canada; Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, 1 King's College Circle, Medical Sciences Building, Toronto, Ontario M5S 1A8, Canada
| | - Alice Rueda
- Interventional Psychiatry Program, St. Michael's Hospital, 193 Yonge Street 6-013, Toronto, Ontario M5B 1M8, Canada
| | - Venkat Bhat
- Interventional Psychiatry Program, St. Michael's Hospital, 193 Yonge Street 6-013, Toronto, Ontario M5B 1M8, Canada; Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, 1 King's College Circle, Medical Sciences Building, Toronto, Ontario M5S 1A8, Canada; Neuroscience Research Program, St. Michael's Hospital, 193 Yonge Street 6-013, Toronto, Ontario M5B 1M8, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, 250 College Street, Toronto, Ontario M5T 1R8, Canada.
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11
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Benrimoh D, Kleinerman A, Furukawa TA, Iii CFR, Lenze EJ, Karp J, Mulsant B, Armstrong C, Mehltretter J, Fratila R, Perlman K, Israel S, Popescu C, Golden G, Qassim S, Anacleto A, Tanguay-Sela M, Kapelner A, Rosenfeld A, Turecki G. Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies. Am J Geriatr Psychiatry 2024; 32:280-292. [PMID: 37839909 DOI: 10.1016/j.jagp.2023.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological and behavioral substrates are associated with treatment response variability. Understanding the sources of this variability and predicting outcomes has been elusive. Machine learning (ML) shows promise in predicting treatment response in MDD, but its application is limited by challenges to the clinical interpretability of ML models, and clinicians often lack confidence in model results. In order to improve the interpretability of ML models in clinical practice, our goal was to demonstrate the derivation of treatment-relevant patient profiles comprised of clinical and demographic information using a novel ML approach. METHODS We analyzed data from six clinical trials of pharmacological treatment for depression (total n = 5438) using the Differential Prototypes Neural Network (DPNN), a ML model that derives patient prototypes which can be used to derive treatment-relevant patient clusters while learning to generate probabilities for differential treatment response. A model classifying remission and outputting individual remission probabilities for five first-line monotherapies and three combination treatments was trained using clinical and demographic data. Prototypes were evaluated for interpretability by assessing differences in feature distributions (e.g. age, sex, symptom severity) and treatment-specific outcomes. RESULTS A 3-prototype model achieved an area under the receiver operating curve of 0.66 and an expected absolute improvement in remission rate for those receiving the best predicted treatment of 6.5% (relative improvement of 15.6%) compared to the population remission rate. We identified three treatment-relevant patient clusters. Cluster A patients tended to be younger, to have increased levels of fatigue, and more severe symptoms. Cluster B patients tended to be older, female, have less severe symptoms, and the highest remission rates. Cluster C patients had more severe symptoms, lower remission rates, more psychomotor agitation, more intense suicidal ideation, and more somatic genital symptoms. CONCLUSION It is possible to produce novel treatment-relevant patient profiles using ML models; doing so may improve interpretability of ML models and the quality of precision medicine treatments for MDD.
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Affiliation(s)
- David Benrimoh
- Department of Psychiatry (DB, KP, GT), McGill University, Montreal, Canada; Department of Psychiatry (DB), Stanford University, Stanford, CA; Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada.
| | | | - Toshi A Furukawa
- Department of Health Promotion and Human Behavior (TAF), Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan
| | - Charles F Reynolds Iii
- Department of Psychiatry (CFR), University of Pittsburgh School of Medicine, Pittsburgh, PA; Department of Psychiatry (CFR), Tufts University School of Medicine, Medford, MA
| | - Eric J Lenze
- Department of Psychiatry (EJL), Washington University School of Medicine, St. Louis, MS
| | - Jordan Karp
- Department of Psychiatry (JK), University of Arizona, Tucson, AZ
| | - Benoit Mulsant
- Department of Psychiatry (BM), University of Toronto, Toronto, ON, Canada
| | - Caitrin Armstrong
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Joseph Mehltretter
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Robert Fratila
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Kelly Perlman
- Department of Psychiatry (DB, KP, GT), McGill University, Montreal, Canada; Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Sonia Israel
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Christina Popescu
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Grace Golden
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Sabrina Qassim
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Alexandra Anacleto
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Myriam Tanguay-Sela
- Aifred Health (DB, CA, JM, RF, KP, SI, CP, GG, SQ, AA, MTS), Montreal, Canada
| | - Adam Kapelner
- Department of Mathematics (AK), Queens College, CUNY, New York, NY
| | | | - Gustavo Turecki
- Department of Psychiatry (DB, KP, GT), McGill University, Montreal, Canada
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12
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Kang Y, Shin D, Kim A, You SH, Kim B, Han KM, Ham BJ. The effect of inflammation markers on cortical thinning in major depressive disorder: A possible mediator of depression and cortical changes. J Affect Disord 2024; 348:229-237. [PMID: 38160887 DOI: 10.1016/j.jad.2023.12.071] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 12/05/2023] [Accepted: 12/27/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is a prevalent mental health condition with significant societal impact. Owing to the intricate biological diversity of MDD, treatment efficacy remains limited. Immune biomarkers have emerged as potential predictors of treatment response, underscoring the interaction between the immune system and the brain. This study investigated the relationship between cytokine levels and cortical thickness in patients with MDD, focusing on the corticolimbic circuit, to elucidate the influence of neuroinflammation on structural brain changes and contribute to a deeper understanding of the pathophysiology of MDD. METHOD A total of 114 patients with MDD and 101 healthy controls (HC) matched for age, sex, and body mass index (BMI) were recruited. All participants were assessed for depression severity using the Hamilton Depression Rating Scale (HDRS), and 3.0 T T1 weighted brain MRI data were acquired. Additionally, cytokine levels were measured using a highly sensitive bead-based multiplex immunosorbent assay. RESULTS Patients diagnosed with MDD exhibited notably elevated levels of interleukin-6 (p = 0.005) and interleukin-8 (p = 0.005), alongside significant cortical thinning in the left anterior cingulate gyrus and left superior frontal gyrus, with these findings maintaining significance even after applying Bonferroni correction. Furthermore, increased interleukin-6 and interleukin-8 levels in patients with MDD are associated with alterations in the left frontomarginal gyrus and right anterior cingulate cortex (ACC). CONCLUSIONS This suggests a potential influence of neuroinflammation on right ACC function in MDD patients, warranting longitudinal research to explore interleukin-6 and interleukin-8 mediated neurotoxicity in MDD vulnerability and brain morphology changes.
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Affiliation(s)
- Youbin Kang
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Daun Shin
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Aram Kim
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Sung-Hye You
- Department of Radiology, Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Byungjun Kim
- Department of Radiology, Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Kyu-Man Han
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea; Department of Psychiatry, 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; Department of Psychiatry, Korea University College of Medicine, Seoul, Republic of Korea.
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13
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Singh A, Arun P, Singh GP, Kaur D, Kaur S. QEEG Predictors of Treatment Response in Major Depressive Disorder- A Replication Study from Northwest India. Clin EEG Neurosci 2024; 55:176-184. [PMID: 36448183 DOI: 10.1177/15500594221142396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Background: Predicting treatment response with antidepressant is a challenging task for clinicians and researchers. An important limitation of an antidepressant trial is the increased time spent before an adequacy of trial can be decided. Quantitative Electroencephalography has shown some evidence in identifying early changes seen with antidepressants. No data has been reported from Indian population on its predictive capabilities. Aim: To examine whether early changes in frontal and prefrontal theta value in QEEG could predict antidepressant treatment response. Methods: Structured clinical assessments were conducted at baseline and after one week in a sample of treatment-seeking adults with major depressive disorder (n = 50). Patients were started on SSRI (Escitalopram, fluoxetine, paroxetine or sertraline) and followed for 8 weeks. QEEG recordings were carried out at baseline and week 1 and its parameters (relative theta power and cordance) were assessed to identify its predictive value for treatment response. Treatment response was assessed using Hamilton depression rating scale with 50% reduction after 8 weeks being considered as response. Results: Mean age of the sample was 39 ± 10 years and majority of them were females (64%). A significant reduction was found in relative frontal theta value (p = 0.021) from baseline to one week in responders. However, linear regression revealed that this change could not predict the treatment response (p = 0.37). Conclusions: QEEG changes are observed in initial phase of antidepressant treatment but these changes can't predict the treatment response.
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Affiliation(s)
- Akashdeep Singh
- Department of Psychiatry, Government Medical College and Hospital, Chandigarh, India
| | - Priti Arun
- Department of Psychiatry, Government Medical College and Hospital, Chandigarh, India
| | - Gurvinder Pal Singh
- Department of Psychiatry, All India Institute of Medical Sciences, Bathinda, Punjab, India
| | - Damanjeet Kaur
- Department of Electrical and Electronic Engineering, University Institute of Engineering and Technology, Chandigarh, India
| | - Simranjit Kaur
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
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14
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Kang HJ, Kim JW, Choi W, Lee JY, Kim SW, Shin IS, Kim JM. Use of Serum Biomarkers to Aid Antidepressant Selection in Depressive Patients. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2024; 22:182-187. [PMID: 38247424 PMCID: PMC10811403 DOI: 10.9758/cpn.23.1071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 04/10/2023] [Indexed: 01/23/2024]
Abstract
Objective : This study aimed to identify serum biomarkers prospectively associated with remission at 12 weeks in out-patients with depressive disorders receiving stepwise psychopharmacotherapy, according to the main antidepressant used during the treatment period. Methods : This study included 1,024 depressive outpatients initially treated using antidepressant monotherapy, followed by alternating pharmacological strategies during the acute phase (3-12 weeks; 3-week interval). Fourteen serum biomarkers, sociodemographics, and clinical characteristics were evaluated at baseline. Based on the use frequency and mechanism of action, four main antidepressant types were distinguished: escitalopram, other selective serotonin reuptake inhibitors (SSRIs), serotonin norepinephrine reuptake inhibitors (SNRIs), and mirtazapine. A Hamilton Depression Rating Scale score ≤ 7 was take to indicate remission. Results : Lower high-sensitivity C-reactive protein levels were correlated with remission at 12 weeks for all antidepressant types. Lower interleukin (IL)-6 levels and tumor necrosis factor-alpha levels were associated with remission using escitalopram and other SSRIs respectively. Lower IL-1β and leptin levels, predicted remission in association with SSRIs including escitalopram. For SNRIs, remission at 12 weeks was predicted by lower IL-4 and IL-10 levels. For mirtazapine, remission at 12 weeks was associated with lower leptin levels, and higher serotonin and folate levels. Conclusion : Baseline serum status, as estimated by nine serum markers, may help clinicians determine the most appropriate antidepressant to achieve remission in the acute phase of depression.
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Affiliation(s)
- Hee-Ju Kang
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Ju-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Wonsuk Choi
- Department of Internal Medicine, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| | - Ju-Yeon Lee
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Il-Seon Shin
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Jae-Min Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
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15
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Berkovitch L, Lee K, Ji JL, Helmer M, Rahmati M, Demšar J, Kraljič A, Matkovič A, Tamayo Z, Murray JD, Repovš G, Krystal JH, Martin WJ, Fonteneau C, Anticevic A. A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.15.23300019. [PMID: 38168378 PMCID: PMC10760263 DOI: 10.1101/2023.12.15.23300019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Importance Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determine effective personalized treatments. Objective To perform a secondary analysis quantifying neural-to-symptom relationships in MDD as a function of antidepressant treatment. Design Double blind randomized controlled trial. Setting Multicenter. Participants Patients with early onset recurrent depression from the public Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Interventions Either sertraline or placebo during 8 weeks (stage 1), and according to response a second line of treatment for 8 additional weeks (stage 2). Main Outcomes and Measures To identify a data-driven pattern of symptom variations during these two stages, we performed a Principal Component Analysis (PCA) on the variations of individual items of four clinical scales measuring depression, anxiety, suicidal ideas and manic-like symptoms, resulting in a univariate measure of clinical improvement. We then investigated how initial clinical and neural factors predicted this measure during stage 1. To do so, we extracted resting-state global brain connectivity (GBC) at baseline at the individual level using a whole-brain functional network parcellation. In turn, we computed a linear model for each brain parcel with individual data-driven clinical improvement scores during stage 1 for each group. Results 192 patients (127 women), age 37.7 years old (standard deviation: 13.5), were included. The first PC (PC1) capturing 20% of clinical variation was similar across treatment groups at stage 1 and stage 2, suggesting a reproducible pattern of symptom improvement. PC1 patients' scores significantly differed according to treatment during stage 1, whereas no difference of response was evidenced between groups with the Clinical Global Impressions (CGI). Baseline GBC correlated to stage 1 PC1 scores in the sertraline, but not in the placebo group. Conclusions and Relevance Using data-driven reduction of symptoms scales, we identified a common profile of symptom improvement across placebo and sertraline. However, the neural patterns of baseline that mapped onto symptom improvement distinguished between treatment and placebo. Our results underscore that mapping from data-driven symptom improvement onto neural circuits is vital to detect treatment-responsive neural profiles that may aid in optimal patient selection for future trials.
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Affiliation(s)
- Lucie Berkovitch
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
- Université Paris Cité, Paris, France
- Department of Psychiatry, GHU Paris Psychiatrie et Neurosciences, Service Hospitalo-Universitaire, Paris, France
- Unicog, Saclay CEA Centre, Neurospin, Gif-Sur-Yvette Cedex, France
| | - Kangjoo Lee
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | - Jie Lisa Ji
- Manifest Technologies, Inc. New Haven, CT, USA
| | | | | | - Jure Demšar
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Aleksij Kraljič
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - Andraž Matkovič
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - Zailyn Tamayo
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | - John D Murray
- Department of Psychological and Brain Science, Dartmouth College, Hanover, NH, USA
| | - Grega Repovš
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - John H Krystal
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | | | - Clara Fonteneau
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | - Alan Anticevic
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
- Department of Psychology, Yale University School of Medicine, New Haven, CT, USA
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16
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von Knorring J, Baryshnikov I, Jylhä P, Talaslahti T, Heikkinen M, Isometsä E. Prospective study of antidepressant treatment of psychiatric patients with depressive disorders: treatment adequacy and outcomes. BMC Psychiatry 2023; 23:888. [PMID: 38017416 PMCID: PMC10683284 DOI: 10.1186/s12888-023-05390-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 11/21/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Despite numerous national depression care guidelines (DCGs), suboptimal antidepressant treatment may occur. We examined DCG concordance and depression treatment outcomes in psychiatric settings. METHODS We evaluated treatment received and outcomes of 128 psychiatric out- and inpatients participating in the PEGAD (Pharmacoepidemiology and Pharmacogenetics of Antidepressant Treatment for Depressive Disorders) study at baseline, two weeks, and eight weeks using interviews and questionnaires. Inclusion criteria were ICD-10 diagnosis of a depressive disorder, a Patient Health Questionnaire-9 symptom (PHQ-9) score ≥ 10, and a new antidepressant prescribed. The primary outcome of the study was within-individual change in PHQ-9 scores. RESULTS At baseline, patients had predominately recurrent (83%) and in 19% treatment-resistant depression (TRD). The median preceding duration of the current episode was 6.5 months. At eight weeks, 85% of the patients (n = 107) used a DCG-concordant antidepressant dose. However, due to the scarcity of antidepressant combinations and augmentations, fewer TRD than non-TRD patients (25% vs. 84%, p < 0.005) received adequate antidepressant treatment. Additionally, one-third of the patients received inadequate follow-up. Overall, only 53% received treatment compatible with DCG recommendations for adequate pharmacotherapy and follow-up. The mean decline in PHQ-9 scores (-3.8 ± SD 5.7) was significant (p < 0.0005). Nearly 40% of the patients reached a subthreshold level of depression (PHQ-9 < 10), predicted by a lower baseline PHQ-9 score, recurrent depression, and female sex. However, 45% experienced no significant clinical improvement (PHQ-9 score reduction < 20%). CONCLUSIONS Our findings suggest that inadequate treatment continues to occur in psychiatric care settings, particularly for TRD patients.
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Affiliation(s)
- Johanna von Knorring
- Department of Psychiatry, University of Helsinki and Helsinki University Hospital, P.O. Box 22, Helsinki, FI-00014, Finland
| | - Ilya Baryshnikov
- Department of Psychiatry, University of Helsinki and Helsinki University Hospital, P.O. Box 22, Helsinki, FI-00014, Finland
| | - Pekka Jylhä
- Department of Psychiatry, University of Helsinki and Helsinki University Hospital, P.O. Box 22, Helsinki, FI-00014, Finland
| | - Tiina Talaslahti
- Department of Psychiatry, University of Helsinki and Helsinki University Hospital, P.O. Box 22, Helsinki, FI-00014, Finland
| | - Martti Heikkinen
- Department of Psychiatry, University of Helsinki and Helsinki University Hospital, P.O. Box 22, Helsinki, FI-00014, Finland
| | - Erkki Isometsä
- Department of Psychiatry, University of Helsinki and Helsinki University Hospital, P.O. Box 22, Helsinki, FI-00014, Finland.
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17
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Yousefian Z, Tamijani SMS, Ghazvini H, Kheirkhah F, Rafaiee R, Mousavi T. Is human cytomegalovirus a potential risk factor for mood disorders? A systematic review and meta-analysis. Indian J Psychiatry 2023; 65:1104-1111. [PMID: 38249142 PMCID: PMC10795672 DOI: 10.4103/indianjpsychiatry.indianjpsychiatry_672_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/19/2023] [Accepted: 11/07/2023] [Indexed: 01/23/2024] Open
Abstract
Mood disorders are among the common mental disorders worldwide. Because of the persistence of cytomegalovirus (CMV) in the body and nervous system, this virus can be activated when the immune system is weakened and continues to exert its destructive effects throughout life. This study aimed to investigate the seroprevalence and association of human cytomegalovirus with mood disorders. Eligible articles were extracted using online international databases Science Direct, Medline, Web of Science, Scopus, and Google Scholar between 2000 and 2023. After quality assessment and specific inclusion and exclusion criteria, a total of eight eligible articles were included in the meta-analysis. Our finding showed that the seropositivity of CMV in mood disorders was 51.6% (95% CI; 42.8-60.4). There were statistical differences between mood disorders and control groups regarding the seropositivity of CMV 1.327% (95% CI; 13.27-10.45). The results of the publication bias using the Egger test confirmed no publication bias in each sub-group. The results of this meta-analysis study demonstrated that CMV infection might have associations with the incidence of mood disorders. Furthermore, we found that there were statistical differences between mood disorders and control groups regarding the seropositivity of CMV.
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Affiliation(s)
- Zahra Yousefian
- Department of Neuroscience, School of Advanced Technologies in Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | | | - Hamed Ghazvini
- Department of Neuroscience, School of Advanced Technologies in Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Farzan Kheirkhah
- Department of Psychiatry, School of Medicine, Babol University of Medical Sciences, Babol, Iran
| | - Raheleh Rafaiee
- Department of Neuroscience, School of Advanced Technologies in Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Tahoora Mousavi
- Molecular and Cell Biology Research Center (MCBRC), Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
- Molecular and Cell Biology Research Center (MCBRC), Hemoglobinopathy Institute, Mazandaran University of Medical Sciences, Sari, Iran
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18
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Oliveira-Maia AJ, Morrens J, Rive B, Godinov Y, Cabrieto J, Perualila N, Barbreau S, Mulhern-Haughey S. ICEBERG study: an indirect adjusted comparison estimating the long-term benefit of esketamine nasal spray when compared with routine treatment of treatment resistant depression in general psychiatry. Front Psychiatry 2023; 14:1250980. [PMID: 38025433 PMCID: PMC10669153 DOI: 10.3389/fpsyt.2023.1250980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/13/2023] [Indexed: 12/01/2023] Open
Abstract
Background Treatment resistant depression (TRD) affects 10-30% of patients with major depressive disorder. In 4-week trials, esketamine nasal spray (NS) was efficacious vs. placebo when both were initiated in addition to a new selective serotonin or serotonin norepinephrine reuptake inhibitor. However, comparison with an extended range of real-world treatments (RWT) is lacking. Methods ICEBERG was an adjusted indirect treatment comparison using propensity score-based inverse probability weighting, performed on 6-month response and remission data from patients receiving esketamine NS plus oral antidepressant from the SUSTAIN-2 (NCT02497287; clinicaltrials.gov) study, compared with patients receiving other RWT from the European Observational TRD Cohort (EOTC; NCT03373253; clinicaltrials.gov) study. SUSTAIN-2 was a long-term open-label study of esketamine NS, while the EOTC was conducted at a time when esketamine NS was not available as RWT. Threshold and sensitivity analyses were conducted to assess how robust the primary analyses were. Results Patients receiving esketamine NS had a higher probability of 6-month response (49.7% [95% confidence interval (CI) 45.6-53.9]) and remission (33.6% [95% CI 29.7-37.6]) vs. patients receiving RWT (26.4% [95% CI 21.5-31.4] and 18.2% [95% CI 13.9-22.5], respectively), according to rescaled average treatment effect among treated estimates. Resulting adjusted odds ratios (OR) and relative risk (RR) favoured esketamine NS over RWT for 6-month response (OR 2.756 [95% CI 2.034-3.733], p < 0.0001; RR 1.882 [95% CI 1.534-2.310], p < 0.0001) and remission (OR 2.276 [95% CI 1.621-3.196], p < 0.0001; RR 1.847 [95% CI 1.418-2.406], p < 0.0001). Threshold analyses suggested that differences between the two studies were robust, and results were consistent across extensive sensitivity analyses. Conclusion ICEBERG supports that, at 6 months, esketamine NS has a substantial and significant benefit over RWT for patients with TRD. While results may be affected by unobserved confounding factors, threshold analyses suggested these were unlikely to impact the study conclusions.To view an animated summary of this publication, please click on the Supplementary video.
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Affiliation(s)
- Albino J. Oliveira-Maia
- Champalimaud Research and Clinical Centre, Champalimaud Foundation, Lisbon, Portugal
- NOVA Medical School, Faculdade de Ciências Médicas, NMS, FCM, Universidade Nova de Lisboa, Lisbon, Portugal
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19
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Tang VM, Yu D, Weissman CR, Jones BDM, Wang G, Sloan ME, Blumberger DM, Daskalakis ZJ, Le Foll B, Voineskos D. Treatment outcomes in major depressive disorder in patients with comorbid alcohol use disorder: A STAR*D analysis. J Affect Disord 2023; 339:691-697. [PMID: 37467796 PMCID: PMC10496139 DOI: 10.1016/j.jad.2023.07.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 06/26/2023] [Accepted: 07/08/2023] [Indexed: 07/21/2023]
Abstract
INTRODUCTION Guidance on Major Depressive Disorder (MDD) treatment in those with comorbid Alcohol Use Disorder (AUD) is limited. We performed a secondary analysis on the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, examining the association between comorbid AUD and depression outcomes. METHODS STAR*D was a real-world effectiveness trial starting with citalopram in level 1. Non-responding participants progressed through 3 other sequential treatment levels with different switch or augmentation options. Antidepressant outcomes were compared between MDD (n = 2826) and comorbid MDD and AUD (n = 864). Logistic regressions were performed to evaluate remission and response predictors in the total STAR*D sample and the AUD-comorbidity interaction. RESULTS Chi-squared tests showed no significant difference in response or remission rates from depression between groups across treatment levels. Higher Hamilton Rating Scale for Depression (HRSD) score was associated with overall lower odds of remission in treatment level 1 (OR = 0.93, p < 0.001) and 2 (OR = 0.95, p < 0.001), with no significant interaction with comorbid AUD. Higher baseline suicidality had overall lower odds of remission in level 1 (OR = 0.82, p < 0.001) and 2 (OR = 0.1, p < 0.001), but with comorbid AUD compared to no AUD, suicidality increased odds of level 1 remission (OR = 1.30, p = 0.012). In comorbid AUD in level 2, venlafaxine was associated with lower odds of remission (OR = 0.13, p = 0.013) and response (OR = 0.12, p = 0.006); bupropion with lower odds of response (OR = 0.22, p = 0.024). LIMITATIONS Open label study design and lack of alcohol use data. CONCLUSIONS Comorbid AUD may interact with predictors of antidepressant response in MDD and using venlafaxine or bupropion may be less effective. Addressing this comorbidity requires unique assessment and treatment approaches.
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Affiliation(s)
- Victor M Tang
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Dengdeng Yu
- Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA
| | - Cory R Weissman
- Department of Psychiatry, UC San Diego Health, La Jolla, CA, USA; Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada
| | - Brett D M Jones
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Guan Wang
- Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Matthew E Sloan
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Daniel M Blumberger
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | | | - Bernard Le Foll
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada; Waypoint Research Institute, Waypoint Centre for Mental Health Care, Penetanguishene, ON, Canada
| | - Daphne Voineskos
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Poul Hansen Family Centre for Depression, Krembil Research Institute, Toronto Western Hospital, University Health Network, Canada.
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20
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Bruzzone SEP, Nasser A, Aripaka SS, Spies M, Ozenne B, Jensen PS, Knudsen GM, Frokjaer VG, Fisher PM. Genetic contributions to brain serotonin transporter levels in healthy adults. Sci Rep 2023; 13:16426. [PMID: 37777558 PMCID: PMC10542378 DOI: 10.1038/s41598-023-43690-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 09/27/2023] [Indexed: 10/02/2023] Open
Abstract
The serotonin transporter (5-HTT) critically shapes serotonin neurotransmission by regulating extracellular brain serotonin levels; it remains unclear to what extent 5-HTT levels in the human brain are genetically determined. Here we applied [11C]DASB positron emission tomography to image brain 5-HTT levels and evaluated associations with five common serotonin-related genetic variants that might indirectly regulate 5-HTT levels (BDNF rs6265, SLC6A4 5-HTTLPR, HTR1A rs6295, HTR2A rs7333412, and MAOA rs1137070) in 140 healthy volunteers. In addition, we explored whether these variants could predict in vivo 5-HTT levels using a five-fold cross-validation random forest framework. MAOA rs1137070 T-carriers showed significantly higher brain 5-HTT levels compared to C-homozygotes (2-11% across caudate, putamen, midbrain, thalamus, hippocampus, amygdala and neocortex). We did not observe significant associations for the HTR1A rs6295 and HTR2A rs7333412 genotypes. Our previously observed lower subcortical 5-HTT availability for rs6265 met-carriers remained in the presence of these additional variants. Despite this significant association, our prediction models showed that genotype moderately improved prediction of 5-HTT in caudate, but effects were not statistically significant after adjustment for multiple comparisons. Our observations provide additional evidence that serotonin-related genetic variants modulate adult human brain serotonin neurotransmission.
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Affiliation(s)
- Silvia Elisabetta Portis Bruzzone
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Arafat Nasser
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Sagar Sanjay Aripaka
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marie Spies
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Brice Ozenne
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Peter Steen Jensen
- 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
| | - Vibe Gedsoe Frokjaer
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Patrick MacDonald Fisher
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.
- Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark.
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21
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Puac-Polanco V, Ziobrowski HN, Ross EL, Liu H, Turner B, Cui R, Leung LB, Bossarte RM, Bryant C, Joormann J, Nierenberg AA, Oslin DW, Pigeon WR, Post EP, Zainal NH, Zaslavsky AM, Zubizarreta JR, Luedtke A, Kennedy CJ, Cipriani A, Furukawa TA, Kessler RC. Development of a model to predict antidepressant treatment response for depression among Veterans. Psychol Med 2023; 53:5001-5011. [PMID: 37650342 PMCID: PMC10519376 DOI: 10.1017/s0033291722001982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). METHODS A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. RESULTS In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. CONCLUSIONS Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.
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Affiliation(s)
| | | | - Eric L. Ross
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Howard Liu
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
| | - Brett Turner
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ruifeng Cui
- VISN 4 Mental Illness Research, Education and Clinical Center, VA Pittsburgh Health Care System, Department of Veterans Affairs, Pittsburgh, PA, USA
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Lucinda B. Leung
- Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Division of General Internal Medicine and Health Services Research, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Robert M. Bossarte
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Department of Behavioral Medicine and Psychiatry, West Virginia University, Morgantown, WV, USA
| | - Corey Bryant
- Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Andrew A. Nierenberg
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Dauten Family Center for Bipolar Treatment Innovation, Massachusetts General Hospital, Boston, MA, USA
| | - David W. Oslin
- VISN 4 Mental Illness Research, Education, and Clinical Center, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Wilfred R. Pigeon
- Center of Excellence for Suicide Prevention, Canandaigua VA Medical Center, Canandaigua, NY, USA
- Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA
| | - Edward P. Post
- Center for Clinical Management Research, VA Ann Arbor, Ann Arbor, MI, USA
- Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Nur Hani Zainal
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Alan M. Zaslavsky
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Jose R. Zubizarreta
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
- Department of Biostatistics, Harvard University, Cambridge, MA, USA
| | - Alex Luedtke
- Department of Statistics, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris J. Kennedy
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | | | - Toshiaki A. Furukawa
- Department of Health Promotion and Human Behavior, School of Public Health, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
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22
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Ekman CJ, Popiolek K, Bodén R, Nordenskjöld A, Lundberg J. Outcome of transcranial magnetic intermittent theta-burst stimulation in the treatment of depression - A Swedish register-based study. J Affect Disord 2023; 329:50-54. [PMID: 36841303 DOI: 10.1016/j.jad.2023.02.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 02/27/2023]
Abstract
BACKGROUND Repetitive transcranial magnetic stimulation (rTMS) is an established treatment of depression. The more recently introduced intermittent Theta-burst stimulation (iTBS) has shown significant superiority over sham-stimulation and equal effect sizes to a 10 Hz protocol in one clinical trial. The aim of the current study was to investigate the effectiveness and tolerability of iTBS in a naturalistic, clinical setting. Further, we explored demographical and clinical predictors of response. METHODS Data was collected from seventeen rTMS-sites in Sweden between January 2018 and May 2021, through the Swedish National Quality register for repetitive Transcranial Magnetic Stimulation (Q-rTMS). We included 542 iTBS-treated patients with unipolar or bipolar depression. Outcome was assessed with Clinical Global Impression Severity and Improvement scores in an intention to treat analysis. RESULTS The response rate was 42.1 % and 16.1 % reached remission. The response rate was significantly larger in the oldest age group compared to the youngest (odds ratio 3.46, 95 % confidence interval 1.65-7.22). Less severe level of depression (Montgomery-Åsberg depression rating scale self-assessment < 36) at baseline predicted response and remission. Only <1 % were much or very much worse after treatment. Drop-out rate was 10.9 %. No serious adverse events were reported. LIMITATIONS Retrospective analysis of register data. No comparison group. CONCLUSIONS In a clinical setting, iTBS was shown to be safe and tolerable and the response rate was similar to that reported from clinical trials. Older age-group and less severe illness predicted response.
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Affiliation(s)
- Carl Johan Ekman
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Sweden.
| | - Katarzyna Popiolek
- University Health Care Research Centre, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Robert Bodén
- Department of Neuroscience, Psychiatry, Uppsala University Hospital, Uppsala, Sweden
| | - Axel Nordenskjöld
- University Health Care Research Centre, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Johan Lundberg
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Region Stockholm, Sweden
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23
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Vos CF, Ter Hark SE, Schellekens AFA, Spijker J, van der Meij A, Grotenhuis AJ, Mihaescu R, Kievit W, Donders R, Aarnoutse RE, Coenen MJH, Janzing JGE. Effectiveness of Genotype-Specific Tricyclic Antidepressant Dosing in Patients With Major Depressive Disorder: A Randomized Clinical Trial. JAMA Netw Open 2023; 6:e2312443. [PMID: 37155164 PMCID: PMC10167565 DOI: 10.1001/jamanetworkopen.2023.12443] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/10/2023] Open
Abstract
Importance Evidence of the clinical benefit of pharmacogenetics-informed treatment (PIT) with antidepressants is still limited. Especially for tricyclic antidepressants (TCAs), pharmacogenetics may be of interest because therapeutic plasma concentrations are well defined, identification of optimal dosing can be time consuming, and treatment is frequently accompanied by adverse effects. Objective To determine whether PIT results in faster attainment of therapeutic TCA plasma concentrations compared with usual treatment in patients with unipolar major depressive disorder (MDD). Design, Setting, and Participants This randomized clinical trial compared PIT with usual treatment among 111 patients at 4 centers in the Netherlands. Patients were treated with the TCAs nortriptyline, clomipramine, or imipramine, with clinical follow-up of 7 weeks. Patients were enrolled from June 1, 2018, to January 1, 2022. At inclusion, patients had unipolar nonpsychotic MDD (with a score of ≥19 on the 17-item Hamilton Rating Scale for Depression [HAMD-17]), were aged 18 to 65 years, and were eligible for TCA treatment. Main exclusion criteria were a bipolar or psychotic disorder, substance use disorder, pregnancy, interacting comedications, and concurrent use of psychotropic medications. Intervention In the PIT group, the initial TCA dosage was based on CYP2D6 and CYP2C19 genotypes. The control group received usual treatment, which comprised the standard initial TCA dosage. Main Outcomes and Measures The primary outcome was days until attainment of a therapeutic TCA plasma concentration. Secondary outcomes were severity of depressive symptoms (measured by HAMD-17 scores) and frequency and severity of adverse effects (measured by Frequency, Intensity, and Burden of Side Effects Rating scores). Results Of 125 patients randomized, 111 (mean [SD] age, 41.7 [13.3] years; 69 [62.2%] female) were included in the analysis; of those, 56 were in the PIT group and 55 were in the control group. The PIT group reached therapeutic concentrations faster than the control group (mean [SD], 17.3 [11.2] vs 22.0 [10.2] days; Kaplan-Meier χ21 = 4.30; P = .04). No significant difference in reduction of depressive symptoms was observed. Linear mixed-model analyses showed that the interaction between group and time differed for the frequency (F6,125 = 4.03; P = .001), severity (F6,114 = 3.10; P = .008), and burden (F6,112 = 2.56; P = .02) of adverse effects, suggesting that adverse effects decreased relatively more for those receiving PIT. Conclusions and Relevance In this randomized clinical trial, PIT resulted in faster attainment of therapeutic TCA concentrations, with potentially fewer and less severe adverse effects. No effect on depressive symptoms was observed. These findings indicate that pharmacogenetics-informed dosing of TCAs can be safely applied and may be useful in personalizing treatment for patients with MDD. Trial Registration ClinicalTrials.gov Identifier: NCT03548675.
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Affiliation(s)
- Cornelis F Vos
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands
| | - Sophie E Ter Hark
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands
| | - Arnt F A Schellekens
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
- Nijmegen Institute for Scientist Practitioners in Addiction, Radboud University, Nijmegen, the Netherlands
| | - Jan Spijker
- Depression Expertise Centre, Pro Persona, Nijmegen, the Netherlands
- Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands
| | | | | | - Raluca Mihaescu
- Department of Psychiatry, Catharina Hospital, Eindhoven, the Netherlands
| | - Wietske Kievit
- Department of Health Evidence, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Rogier Donders
- Department of Health Evidence, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Rob E Aarnoutse
- Department of Pharmacy, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Marieke J H Coenen
- Department of Clinical Chemistry, Erasmus University Medical Centre, Rotterdam, the Netherlands
| | - Joost G E Janzing
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
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24
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Zimmerman M, Mackin DM. Reliability and validity of the difficult to treat depression questionnaire (DTDQ). Psychiatry Res 2023; 324:115225. [PMID: 37116322 DOI: 10.1016/j.psychres.2023.115225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 04/16/2023] [Accepted: 04/23/2023] [Indexed: 04/30/2023]
Abstract
It has recently been recommended that treatment resistant depression be reconceptualized and renamed as difficult to treat depression (DTD). A consensus statement by an expert panel identified multiple variables associated with DTD and emphasized the importance of conducting a comprehensive evaluation of patients to identify predictors of inadequate treatment response. For practical reasons, it would be desirable to develop a self-report scale that can be incorporated into clinical practice that identifies patient, clinical, and treatment risk factors for DTD. Nine hundred twenty depressed patients completed the Difficult to Treat Depression Questionnaire (DTDQ). A subset of patients completed the scale a second time and completed the Remission from Depression Questionnaire at admission and discharge from a partial hospital program. The DTDQ demonstrated excellent internal consistency and test-retest reliability. Both the total DTDQ and the number of prior failed medication trials, the metric primarily relied upon to classify treatment resistant depression, predicted outcome. However, the DTDQ continued to be significantly associated with outcome after controlling for the number of failed trials, whereas the number of failed trials did not predict outcome after controlling for DTDQ scores. The DTDQ is a reliable and valid measure of the recently discussed concept of DTD.
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Affiliation(s)
- Mark Zimmerman
- Department of Psychiatry and Human Behavior, Brown Medical School, Rhode Island Hospital, Providence, RI, United States.
| | - Daniel M Mackin
- Department of Psychiatry and Human Behavior, Brown Medical School, Rhode Island Hospital, Providence, RI, United States
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Rost N, Dwyer DB, Gaffron S, Rechberger S, Maier D, Binder EB, Brückl TM. Multimodal predictions of treatment outcome in major depression: A comparison of data-driven predictors with importance ratings by clinicians. J Affect Disord 2023; 327:330-339. [PMID: 36750160 DOI: 10.1016/j.jad.2023.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 01/23/2023] [Accepted: 02/01/2023] [Indexed: 02/08/2023]
Abstract
BACKGROUND Reliable prediction models of treatment outcome in Major Depressive Disorder (MDD) are currently lacking in clinical practice. Data-driven outcome definitions, combining data from multiple modalities and incorporating clinician expertise might improve predictions. METHODS We used unsupervised machine learning to identify treatment outcome classes in 1060 MDD inpatients. Subsequently, classification models were created on clinical and biological baseline information to predict treatment outcome classes and compared to the performance of two widely used classical outcome definitions. We also related the findings to results from an online survey that assessed which information clinicians use for outcome prognosis. RESULTS Three and four outcome classes were identified by unsupervised learning. However, data-driven outcome classes did not result in more accurate prediction models. The best prediction model was targeting treatment response in its standard definition and reached accuracies of 63.9 % in the test sample, and 59.5 % and 56.9 % in the validation samples. Top predictors included sociodemographic and clinical characteristics, while biological parameters did not improve prediction accuracies. Treatment history, personality factors, prior course of the disorder, and patient attitude towards treatment were ranked as most important indicators by clinicians. LIMITATIONS Missing data limited the power to identify biological predictors of treatment outcome from certain modalities. CONCLUSIONS So far, the inclusion of available biological measures in addition to psychometric and clinical information did not improve predictive value of the models, which was overall low. Optimized biomarkers, stratified predictions and the inclusion of clinical expertise may improve future prediction models.
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Affiliation(s)
- Nicolas Rost
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany; International Max Planck Research School for Translational Psychiatry, Munich, Germany.
| | - Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich, Germany; Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | | | | | | | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Tanja M Brückl
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
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26
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Jensen KHR, Dam VH, Ganz M, Fisher PM, Ip CT, Sankar A, Marstrand-Joergensen MR, Ozenne B, Osler M, Penninx BWJH, Pinborg LH, Frokjaer VG, Knudsen GM, Jørgensen MB. Deep phenotyping towards precision psychiatry of first-episode depression - the Brain Drugs-Depression cohort. BMC Psychiatry 2023; 23:151. [PMID: 36894940 PMCID: PMC9999625 DOI: 10.1186/s12888-023-04618-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 02/19/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Major Depressive Disorder (MDD) is a heterogenous brain disorder, with potentially multiple psychosocial and biological disease mechanisms. This is also a plausible explanation for why patients do not respond equally well to treatment with first- or second-line antidepressants, i.e., one-third to one-half of patients do not remit in response to first- or second-line treatment. To map MDD heterogeneity and markers of treatment response to enable a precision medicine approach, we will acquire several possible predictive markers across several domains, e.g., psychosocial, biochemical, and neuroimaging. METHODS All patients are examined before receiving a standardised treatment package for adults aged 18-65 with first-episode depression in six public outpatient clinics in the Capital Region of Denmark. From this population, we will recruit a cohort of 800 patients for whom we will acquire clinical, cognitive, psychometric, and biological data. A subgroup (subcohort I, n = 600) will additionally provide neuroimaging data, i.e., Magnetic Resonance Imaging, and Electroencephalogram, and a subgroup of patients from subcohort I unmedicated at inclusion (subcohort II, n = 60) will also undergo a brain Positron Emission Tomography with the [11C]-UCB-J tracer binding to the presynaptic glycoprotein-SV2A. Subcohort allocation is based on eligibility and willingness to participate. The treatment package typically lasts six months. Depression severity is assessed with the Quick Inventory of Depressive Symptomatology (QIDS) at baseline, and 6, 12 and 18 months after treatment initiation. The primary outcome is remission (QIDS ≤ 5) and clinical improvement (≥ 50% reduction in QIDS) after 6 months. Secondary endpoints include remission at 12 and 18 months and %-change in QIDS, 10-item Symptom Checklist, 5-item WHO Well-Being Index, and modified Disability Scale from baseline through follow-up. We also assess psychotherapy and medication side-effects. We will use machine learning to determine a combination of characteristics that best predict treatment outcomes and statistical models to investigate the association between individual measures and clinical outcomes. We will assess associations between patient characteristics, treatment choices, and clinical outcomes using path analysis, enabling us to estimate the effect of treatment choices and timing on the clinical outcome. DISCUSSION The BrainDrugs-Depression study is a real-world deep-phenotyping clinical cohort study of first-episode MDD patients. TRIAL REGISTRATION Registered at clinicaltrials.gov November 15th, 2022 (NCT05616559).
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Affiliation(s)
- Kristian Høj Reveles Jensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Vibeke H Dam
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Patrick MacDonald Fisher
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Cheng-Teng Ip
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Center for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Anjali Sankar
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Maja Rou Marstrand-Joergensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, 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
| | - Merete Osler
- Center for Clinical Research and Prevention, Bispebjerg & Frederiksberg Hospitals, Copenhagen, Denmark.,Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Lars H Pinborg
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Vibe Gedsø Frokjaer
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Gitte Moos Knudsen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Martin Balslev Jørgensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark. .,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark. .,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. .,Psychiatric Centre Copenhagen, Copenhagen, Denmark.
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27
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Guo L, Li J, White H, Xu Z, Ren J, Huang X, Chen Y, Yang K. PROTOCOL: Treatment for depressive disorder among adults: An evidence and gap map of systematic reviews. CAMPBELL SYSTEMATIC REVIEWS 2023; 19:e1308. [PMID: 36911856 PMCID: PMC9985796 DOI: 10.1002/cl2.1308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
This is the protocol for a Campbell evidence and gap map. The objective of the map is to map available systematic reviews on the effectiveness of treatments for depressive disorders among adults. Specifically, this EGM includes studies on the effectiveness of treatments across a range of outcome domains.
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Affiliation(s)
- Liping Guo
- Evidence‐Based Medicine Center, The Centre of Evidence‐based Social Science, School of Basic MedicineLanzhou UniversityLanzhouChina
| | - Jieyun Li
- Evidence‐Based Medicine Center, The Centre of Evidence‐based Social Science, School of Basic MedicineLanzhou UniversityLanzhouChina
| | - Howard White
- Evidence‐Based Medicine Center, The Centre of Evidence‐based Social Science, School of Basic MedicineLanzhou UniversityLanzhouChina
| | - Zheng Xu
- Evidence‐Based Medicine Center, The Centre of Evidence‐based Social Science, School of Basic MedicineLanzhou UniversityLanzhouChina
| | - Junjie Ren
- The Centre of Evidence‐based Social Science, School of Public healthLanzhou UniversityLanzhouChina
| | - Xinyu Huang
- The Centre of Evidence‐based Social Science, School of Public healthLanzhou UniversityLanzhouChina
| | - Yaogeng Chen
- School of Basic MedicineNingxia Medical UniversityNingxiaChina
| | - Kehu Yang
- Evidence‐Based Medicine Center, The Centre of Evidence‐based Social Science, School of Basic MedicineLanzhou UniversityLanzhouChina
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28
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Sathyanarayanan A, Mueller TT, Ali Moni M, Schueler K, Baune BT, Lio P, Mehta D, Baune BT, Dierssen M, Ebert B, Fabbri C, Fusar-Poli P, Gennarelli M, Harmer C, Howes OD, Janzing JGE, Lio P, Maron E, Mehta D, Minelli A, Nonell L, Pisanu C, Potier MC, Rybakowski F, Serretti A, Squassina A, Stacey D, van Westrhenen R, Xicota L. Multi-omics data integration methods and their applications in psychiatric disorders. Eur Neuropsychopharmacol 2023; 69:26-46. [PMID: 36706689 DOI: 10.1016/j.euroneuro.2023.01.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/22/2022] [Accepted: 01/02/2023] [Indexed: 01/27/2023]
Abstract
To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.
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Affiliation(s)
- Anita Sathyanarayanan
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Tamara T Mueller
- Institute for Artificial Intelligence and Informatics in Medicine, TU Munich, 80333 Munich, Germany
| | - Mohammad Ali Moni
- Artificial Intelligence and Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Katja Schueler
- Clinic for Psychosomatics, Hospital zum Heiligen Geist, Frankfurt am Main, Germany; Frankfurt Psychoanalytic Institute, Frankfurt am Main, Germany
| | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia.
| | | | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Mara Dierssen
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Bjarke Ebert
- Medical Strategy & Communication, H. Lundbeck A/S, Valby, Denmark
| | - Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Intervention and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, King's College London, United Kingdom; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Oliver D Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Psychiatric Imaging, Medical Research Council Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | | | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Eduard Maron
- Department of Psychiatry, University of Tartu, Tartu, Estonia; Centre for Neuropsychopharmacology, Division of Brain Sciences, Imperial College London, London, United Kingdom; Documental Ltd, Tallin, Estonia; West Tallinn Central Hospital, Tallinn, Estonia
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Alessandra Minelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Lara Nonell
- MARGenomics, IMIM (Hospital del Mar Research Institute), Barcelona, Spain
| | - Claudia Pisanu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | | | - Filip Rybakowski
- Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Alessio Squassina
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - David Stacey
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Roos van Westrhenen
- Parnassia Psychiatric Institute, Amsterdam, the Netherlands; Department of Psychiatry and Neuropsychology, Faculty of Health and Sciences, Maastricht University, Maastricht, the Netherlands; Institute of Psychiatry, Psychology & Neuroscience (IoPPN) King's College London, United Kingdom
| | - Laura Xicota
- Paris Brain Institute ICM, Salpetriere Hospital, Paris, France
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29
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Lee CT, Palacios J, Richards D, Hanlon AK, Lynch K, Harty S, Claus N, Swords L, O’Keane V, Stephan KE, Gillan CM. The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation. BMC Psychiatry 2023; 23:25. [PMID: 36627607 PMCID: PMC9832676 DOI: 10.1186/s12888-022-04462-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 12/09/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Evidence-based treatments for depression exist but not all patients benefit from them. Efforts to develop predictive models that can assist clinicians in allocating treatments are ongoing, but there are major issues with acquiring the volume and breadth of data needed to train these models. We examined the feasibility, tolerability, patient characteristics, and data quality of a novel protocol for internet-based treatment research in psychiatry that may help advance this field. METHODS A fully internet-based protocol was used to gather repeated observational data from patient cohorts receiving internet-based cognitive behavioural therapy (iCBT) (N = 600) or antidepressant medication treatment (N = 110). At baseline, participants provided > 600 data points of self-report data, spanning socio-demographics, lifestyle, physical health, clinical and other psychological variables and completed 4 cognitive tests. They were followed weekly and completed another detailed clinical and cognitive assessment at week 4. In this paper, we describe our study design, the demographic and clinical characteristics of participants, their treatment adherence, study retention and compliance, the quality of the data gathered, and qualitative feedback from patients on study design and implementation. RESULTS Participant retention was 92% at week 3 and 84% for the final assessment. The relatively short study duration of 4 weeks was sufficient to reveal early treatment effects; there were significant reductions in 11 transdiagnostic psychiatric symptoms assessed, with the largest improvement seen for depression. Most participants (66%) reported being distracted at some point during the study, 11% failed 1 or more attention checks and 3% consumed an intoxicating substance. Data quality was nonetheless high, with near perfect 4-week test retest reliability for self-reported height (ICC = 0.97). CONCLUSIONS An internet-based methodology can be used efficiently to gather large amounts of detailed patient data during iCBT and antidepressant treatment. Recruitment was rapid, retention was relatively high and data quality was good. This paper provides a template methodology for future internet-based treatment studies, showing that such an approach facilitates data collection at a scale required for machine learning and other data-intensive methods that hope to deliver algorithmic tools that can aid clinical decision-making in psychiatry.
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Affiliation(s)
- Chi Tak Lee
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,grid.8217.c0000 0004 1936 9705Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Jorge Palacios
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,grid.487403.c0000 0004 7474 9161SilverCloud Science, SilverCloud Health, Dublin, Ireland
| | - Derek Richards
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,grid.487403.c0000 0004 7474 9161SilverCloud Science, SilverCloud Health, Dublin, Ireland
| | - Anna K. Hanlon
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,grid.8217.c0000 0004 1936 9705Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Kevin Lynch
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,grid.8217.c0000 0004 1936 9705Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Siobhan Harty
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland ,grid.8217.c0000 0004 1936 9705Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland ,grid.487403.c0000 0004 7474 9161SilverCloud Science, SilverCloud Health, Dublin, Ireland
| | - Nathalie Claus
- grid.5252.00000 0004 1936 973XDepartment of Psychology, Division of Clinical Psychology and Psychological Treatment, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Lorraine Swords
- grid.8217.c0000 0004 1936 9705School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Veronica O’Keane
- grid.8217.c0000 0004 1936 9705Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland ,grid.8217.c0000 0004 1936 9705School of Medicine, Trinity College Dublin, Dublin, Ireland ,grid.413305.00000 0004 0617 5936Tallaght Hospital, Trinity Centre for Health Sciences, Tallaght University Hospital, Tallaght, Dublin, Ireland
| | - Klaas E Stephan
- grid.5801.c0000 0001 2156 2780Institute for Biomedical Engineering, Translational Neuromodeling Unit, University of Zürich & Eidgenössische Technische Hochschule, Zurich, Switzerland ,grid.418034.a0000 0004 4911 0702Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Claire M Gillan
- School of Psychology, Trinity College Dublin, Dublin, Ireland. .,Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland. .,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.
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Ford T, Buchanan DM, Azeez A, Benrimoh DA, Kaloiani I, Bandeira ID, Hunegnaw S, Lan L, Gholmieh M, Buch V, Williams NR. Taking modern psychiatry into the metaverse: Integrating augmented, virtual, and mixed reality technologies into psychiatric care. Front Digit Health 2023; 5:1146806. [PMID: 37035477 PMCID: PMC10080019 DOI: 10.3389/fdgth.2023.1146806] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 02/27/2023] [Indexed: 04/11/2023] Open
Abstract
The landscape of psychiatry is ever evolving and has recently begun to be influenced more heavily by new technologies. One novel technology which may have particular application to psychiatry is the metaverse, a three-dimensional digital social platform accessed via augmented, virtual, and mixed reality (AR/VR/MR). The metaverse allows the interaction of users in a virtual world which can be measured and manipulated, posing at once exciting new possibilities and significant potential challenges and risks. While the final form of the nascent metaverse is not yet clear, the immersive simulation and holographic mixed reality-based worlds made possible by the metaverse have the potential to redefine neuropsychiatric care for both patients and their providers. While a number of applications for this technology can be envisioned, this article will focus on leveraging the metaverse in three specific domains: medical education, brain stimulation, and biofeedback. Within medical education, the metaverse could allow for more precise feedback to students performing patient interviews as well as the ability to more easily disseminate highly specialized technical skills, such as those used in advanced neurostimulation paradigms. Examples of potential applications in brain stimulation and biofeedback range from using AR to improve precision targeting of non-invasive neuromodulation modalities to more innovative practices, such as using physiological and behavioral measures derived from interactions in VR environments to directly inform and personalize treatment parameters for patients. Along with promising future applications, we also discuss ethical implications and data security concerns that arise when considering the introduction of the metaverse and related AR/VR technologies to psychiatric research and care.
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Affiliation(s)
- T.J. Ford
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - Derrick M. Buchanan
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
- Correspondence: Derrick M. Buchanan
| | - Azeezat Azeez
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - David A. Benrimoh
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Irakli Kaloiani
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - Igor D. Bandeira
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - Saron Hunegnaw
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - Lucy Lan
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - Mia Gholmieh
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
| | - Vivek Buch
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
- Neurosurgery, Stanford University, Palo Alto, CA, United States
| | - Nolan R. Williams
- Brain Stimulation Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, United States
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Rost N, Binder EB, Brückl TM. Predicting treatment outcome in depression: an introduction into current concepts and challenges. Eur Arch Psychiatry Clin Neurosci 2023; 273:113-127. [PMID: 35587279 PMCID: PMC9957888 DOI: 10.1007/s00406-022-01418-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 04/11/2022] [Indexed: 12/19/2022]
Abstract
Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate goal. Even though numerous studies have investigated patient-specific indicators of treatment efficacy, no (bio)markers or empirical tests for use in clinical practice have resulted as of now. Therefore, clinical decisions regarding the treatment of MDD still have to be made on the basis of questionnaire- or interview-based assessments and general guidelines without the support of a (laboratory) test. We conducted a narrative review of current approaches to characterize and predict outcome to pharmacological treatments in MDD. We particularly focused on findings from newer computational studies using machine learning and on the resulting implementation into clinical decision support systems. The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. We outline several challenges that need to be tackled on different stages of the translational process, from current concepts and definitions to generalizable prediction models and their successful implementation into digital support systems. By bridging the addressed gaps in translational psychiatric research, advances in data quantity and new technologies may enable the next steps toward precision psychiatry.
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Affiliation(s)
- Nicolas Rost
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany. .,International Max Planck Research School for Translational Psychiatry, Munich, Germany.
| | - Elisabeth B. Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804 Munich, Germany
| | - Tanja M. Brückl
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804 Munich, Germany
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Smart SE, Agbedjro D, Pardiñas AF, Ajnakina O, Alameda L, Andreassen OA, Barnes TRE, Berardi D, Camporesi S, Cleusix M, Conus P, Crespo-Facorro B, D'Andrea G, Demjaha A, Di Forti M, Do K, Doody G, Eap CB, Ferchiou A, Guidi L, Homman L, Jenni R, Joyce E, Kassoumeri L, Lastrina O, Melle I, Morgan C, O'Neill FA, Pignon B, Restellini R, Richard JR, Simonsen C, Španiel F, Szöke A, Tarricone I, Tortelli A, Üçok A, Vázquez-Bourgon J, Murray RM, Walters JTR, Stahl D, MacCabe JH. Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium. Schizophr Res 2022; 250:1-9. [PMID: 36242784 PMCID: PMC9834064 DOI: 10.1016/j.schres.2022.09.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 08/03/2022] [Accepted: 09/04/2022] [Indexed: 11/06/2022]
Abstract
INTRODUCTION Our aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR. METHODS We combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction. RESULTS Our sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %). IMPLICATIONS Our findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.
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Affiliation(s)
- Sophie E Smart
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK; Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
| | - Deborah Agbedjro
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Olesya Ajnakina
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London, UK
| | - Luis Alameda
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; Centro de Investigacion en Red Salud Mental (CIBERSAM), Sevilla, Spain; Department of Psychiatry, Hospital Universitario Virgen del Rocio, IBiS, Universidad de Sevilla, Spain; TIPP (Treatment and Early Intervention in Psychosis Program), Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | | | - Domenico Berardi
- Department of Biomedical and Neuro-motor Sciences, Psychiatry Unit, Alma Mater Studiorum Università di Bologna, Bologna, Italy
| | - Sara Camporesi
- TIPP (Treatment and Early Intervention in Psychosis Program), Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland; Unit for Research in Schizophrenia, Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Martine Cleusix
- Unit for Research in Schizophrenia, Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Philippe Conus
- TIPP (Treatment and Early Intervention in Psychosis Program), Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Benedicto Crespo-Facorro
- Centro de Investigacion en Red Salud Mental (CIBERSAM), Sevilla, Spain; Department of Psychiatry, Hospital Universitario Virgen del Rocio, IBiS, Universidad de Sevilla, Spain
| | - Giuseppe D'Andrea
- Department of Biomedical and Neuro-motor Sciences, Psychiatry Unit, Alma Mater Studiorum Università di Bologna, Bologna, Italy
| | - Arsime Demjaha
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Marta Di Forti
- Social Genetics and Developmental Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
| | - Kim Do
- Unit for Research in Schizophrenia, Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Gillian Doody
- Department of Medical Education, University of Nottingham Faculty of Medicine and Health Sciences, Nottingham, UK
| | - Chin B Eap
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Prilly, Switzerland; School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland; Center for Research and Innovation in Clinical Pharmaceutical Sciences, University of Lausanne, Switzerland; Institute of Pharmaceutical Sciences of Western, Switzerland, University of Geneva, University of Lausanne
| | - Aziz Ferchiou
- Univ Paris Est Creteil, INSERM, IMRB, Translational Neuropsychiatry, Fondation FondaMental, Creteil, France; AP-HP, Hôpitaux Universitaires H. Mondor, DMU IMPACT, FHU ADAPT, Creteil, France
| | - Lorenzo Guidi
- Department of Medical and Surgical Sciences, Bologna Transcultural Psychosomatic Team (BoTPT), Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Lina Homman
- Disability Research Division (FuSa), Department of Behavioural Sciences and Learning, Linköping University, Linköping, Sweden
| | - Raoul Jenni
- Unit for Research in Schizophrenia, Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Eileen Joyce
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Laura Kassoumeri
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Ornella Lastrina
- Department of Medical and Surgical Sciences, Bologna Transcultural Psychosomatic Team (BoTPT), Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Ingrid Melle
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Craig Morgan
- Health Service and Population Research, King's College London, London, UK; Centre for Society and Mental Health, King's College London, London, UK
| | - Francis A O'Neill
- Centre for Public Health, Institute of Clinical Sciences, Queens University Belfast, Belfast, UK
| | - Baptiste Pignon
- Univ Paris Est Creteil, INSERM, IMRB, Translational Neuropsychiatry, Fondation FondaMental, Creteil, France; AP-HP, Hôpitaux Universitaires H. Mondor, DMU IMPACT, FHU ADAPT, Creteil, France
| | - Romeo Restellini
- TIPP (Treatment and Early Intervention in Psychosis Program), Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland; Unit for Research in Schizophrenia, Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Jean-Romain Richard
- Univ Paris Est Creteil, INSERM, IMRB, Translational Neuropsychiatry, Fondation FondaMental, Creteil, France
| | - Carmen Simonsen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Early Intervention in Psychosis Advisory Unit for South East Norway (TIPS Sør-Øst), Division of Mental Health and Addiction, Oslo University Hospital, Norway
| | - Filip Španiel
- Department of Applied Neuroscience and Neuroimaging, National Institute of Mental Health, Klecany, Czechia; Department of Psychiatry and Medical Psychology, Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Andrei Szöke
- Univ Paris Est Creteil, INSERM, IMRB, Translational Neuropsychiatry, Fondation FondaMental, Creteil, France; AP-HP, Hôpitaux Universitaires H. Mondor, DMU IMPACT, FHU ADAPT, Creteil, France
| | - Ilaria Tarricone
- Department of Medical and Surgical Sciences, Bologna Transcultural Psychosomatic Team (BoTPT), Alma Mater Studiorum - University of Bologna, Bologna, Italy
| | - Andrea Tortelli
- Univ Paris Est Creteil, INSERM, IMRB, Translational Neuropsychiatry, Fondation FondaMental, Creteil, France; Groupe Hospitalier Universitaire Psychiatrie Neurosciences Paris, Pôle Psychiatrie Précarité, Paris, France
| | - Alp Üçok
- Istanbul University, Istanbul Faculty of Medicine, Department of Psychiatry, Istanbul, Turkey
| | - Javier Vázquez-Bourgon
- Centro de Investigacion en Red Salud Mental (CIBERSAM), Sevilla, Spain; Department of Psychiatry, University Hospital Marques de Valdecilla - Instituto de Investigación Marques de Valdecilla (IDIVAL), Santander, Spain; Department of Medicine and Psychiatry, School of Medicine, University of Cantabria, Santander, Spain
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - James T R Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Daniel Stahl
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - James H MacCabe
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK; South London and Maudsley NHS Foundation Trust, London, UK
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Perez TM, Glue P, Adhia DB, Navid MS, Zeng J, Dillingham P, Smith M, Niazi IK, Young CK, De Ridder D. Infraslow closed-loop brain training for anxiety and depression (ISAD): a protocol for a randomized, double-blind, sham-controlled pilot trial in adult females with internalizing disorders. Trials 2022; 23:949. [DOI: 10.1186/s13063-022-06863-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 10/22/2022] [Indexed: 11/18/2022] Open
Abstract
Abstract
Background
The core intrinsic connectivity networks (core-ICNs), encompassing the default-mode network (DMN), salience network (SN) and central executive network (CEN), have been shown to be dysfunctional in individuals with internalizing disorders (IDs, e.g. major depressive disorder, MDD; generalized anxiety disorder, GAD; social anxiety disorder, SOC). As such, source-localized, closed-loop brain training of electrophysiological signals, also known as standardized low-resolution electromagnetic tomography (sLORETA) neurofeedback (NFB), targeting key cortical nodes within these networks has the potential to reduce symptoms associated with IDs and restore normal core ICN function. We intend to conduct a randomized, double-blind (participant and assessor), sham-controlled, parallel-group (3-arm) trial of sLORETA infraslow (<0.1 Hz) fluctuation neurofeedback (sLORETA ISF-NFB) 3 times per week over 4 weeks in participants (n=60) with IDs. Our primary objectives will be to examine patient-reported outcomes (PROs) and neurophysiological measures to (1) compare the potential effects of sham ISF-NFB to either genuine 1-region ISF-NFB or genuine 2-region ISF-NFB, and (2) assess for potential associations between changes in PRO scores and modifications of electroencephalographic (EEG) activity/connectivity within/between the trained regions of interest (ROIs). As part of an exploratory analysis, we will investigate the effects of additional training sessions and the potential for the potentiation of the effects over time.
Methods
We will randomly assign participants who meet the criteria for MDD, GAD, and/or SOC per the MINI (Mini International Neuropsychiatric Interview for DSM-5) to one of three groups: (1) 12 sessions of posterior cingulate cortex (PCC) ISF-NFB up-training (n=15), (2) 12 sessions of concurrent PCC ISF up-training and dorsal anterior cingulate cortex (dACC) ISF-NFB down-training (n=15), or (3) 6 sessions of yoked-sham training followed by 6 sessions genuine ISF-NFB (n=30). Transdiagnostic PROs (Hospital Anxiety and Depression Scale, HADS; Inventory of Depression and Anxiety Symptoms – Second Version, IDAS-II; Multidimensional Emotional Disorder Inventory, MEDI; Intolerance of Uncertainty Scale – Short Form, IUS-12; Repetitive Thinking Questionnaire, RTQ-10) as well as resting-state neurophysiological measures (full-band EEG and ECG) will be collected from all subjects during two baseline sessions (approximately 1 week apart) then at post 6 sessions, post 12 sessions, and follow-up (1 month later). We will employ Bayesian methods in R and advanced source-localisation software (i.e. exact low-resolution brain electromagnetic tomography; eLORETA) in our analysis.
Discussion
This protocol will outline the rationale and research methodology for a clinical pilot trial of sLORETA ISF-NFB targeting key nodes within the core-ICNs in a female ID population with the primary aims being to assess its potential efficacy via transdiagnostic PROs and relevant neurophysiological measures.
Trial registration
Our study was prospectively registered with the Australia New Zealand Clinical Trials Registry (ANZCTR; Trial ID: ACTRN12619001428156). Registered on October 15, 2019.
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Tanguay-Sela M, Rollins C, Perez T, Qiang V, Golden G, Tunteng JF, Perlman K, Simard J, Benrimoh D, Margolese HC. A systematic meta-review of patient-level predictors of psychological therapy outcome in major depressive disorder. J Affect Disord 2022; 317:307-318. [PMID: 36029877 DOI: 10.1016/j.jad.2022.08.041] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 08/16/2022] [Accepted: 08/19/2022] [Indexed: 10/31/2022]
Abstract
BACKGROUND Psychological therapies are effective for treating major depressive disorder, but current clinical guidelines do not provide guidance on the personalization of treatment choice. Established predictors of psychotherapy treatment response could help inform machine learning models aimed at predicting individual patient responses to different therapy options. Here we sought to comprehensively identify known predictors. METHODS EMBASE, Medline, PubMed, PsycINFO were searched for systematic reviews with or without meta-analysis published until June 2020 to identify individual patient-level predictors of response to psychological treatments. 3113 abstracts were identified and 300 articles assessed. We qualitatively synthesized our findings by predictor category (sociodemographic; symptom profile; social support; personality features; affective, cognitive, and behavioural; comorbidities; neuroimaging; genetics) and treatment type. We used the AMSTAR 2 to evaluate the quality of included reviews. RESULTS Following screening and full-text assessment, 27 systematic reviews including 12 meta-analyses were eligible for inclusion. 74 predictors emerged for various psychological treatments, primarily cognitive behavioural therapy, interpersonal therapy, and mindfulness-based cognitive therapy. LIMITATIONS A paucity of studies examining predictors of psychological treatment outcome, as well as methodological heterogeneities and publication biases limit the strength of the identified predictors. CONCLUSIONS The synthesized predictors could be used to supplement clinical decision-making in selecting psychological therapies based on individual patient characteristics. These predictors could also be used as a priori input features for machine learning models aimed at predicting a given patient's likelihood of response to different treatment options for depression, and may contribute toward the development of patient-specific treatment recommendations in clinical guidelines.
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Affiliation(s)
| | | | | | | | | | | | | | - Jade Simard
- Université du Québec à Montréal, Montreal, Quebec, Canada
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Karvelis P, Charlton CE, Allohverdi SG, Bedford P, Hauke DJ, Diaconescu AO. Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review. Netw Neurosci 2022; 6:1066-1103. [PMID: 38800454 PMCID: PMC11117101 DOI: 10.1162/netn_a_00233] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/14/2022] [Indexed: 05/29/2024] Open
Abstract
Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.
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Affiliation(s)
- Povilas Karvelis
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Colleen E. Charlton
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Shona G. Allohverdi
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Peter Bedford
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Daniel J. Hauke
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Andreea O. Diaconescu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
- University of Toronto, Department of Psychiatry, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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Prediction of remission among patients with a major depressive disorder based on the resting-state functional connectivity of emotion regulation networks. Transl Psychiatry 2022; 12:391. [PMID: 36115833 PMCID: PMC9482642 DOI: 10.1038/s41398-022-02152-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 08/29/2022] [Accepted: 09/02/2022] [Indexed: 11/08/2022] Open
Abstract
The prediction of antidepressant response is critical for psychiatrists to select the initial antidepressant drug for patients with major depressive disorders (MDD). The implicated brain networks supporting emotion regulation (ER) are critical in the pathophysiology of MDD and the prediction of antidepressant response. Therefore, the primary aim of the current study was to identify the neuroimaging biomarkers for the prediction of remission in patients with MDD based on the resting-state functional connectivity (rsFC) of the ER networks. A total of 81 unmedicated adult MDD patients were investigated and they underwent resting-state functional magnetic resonance imagining (fMRI) scans. The patients were treated with escitalopram for 12 weeks. The 17-item Hamilton depression rating scale was used for assessing remission. The 36 seed regions from predefined ER networks were selected and the rsFC matrix was caculated for each participant. The support vector machine algorithm was employed to construct prediction model, which separated the patients with remission from those with non-remission. And leave-one-out cross-validation and the area under the curve (AUC) of the receiver operating characteristic were used for evaluating the performance of the model. The accuracy of the prediction model was 82.08% (sensitivity = 71.43%, specificity = 89.74%, AUC = 0.86). The rsFC between the left medial superior frontal gyrus and the right inferior frontal gyrus as well as the precuneus were the features with the highest discrimination ability in predicting remission from escitalopram among the MDD patients. Results from our study demonstrated that rsFC of the ER brain networks are potential predictors for the response of antidepressant drugs. The trial name: appropriate technology study of MDD diagnosis and treatment based on objective indicators and measurement. URL: http://www.chictr.org.cn/showproj.aspx?proj=21377 . Registration number: ChiCTR-OOC-17012566.
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Xue L, Shao J, Wang H, Wang X, Zhu R, Yao Z, Lu Q. Shared and unique imaging-derived endo-phenotypes of two typical antidepressant-applicative depressive patients. Eur Radiol 2022; 33:645-655. [PMID: 35980436 DOI: 10.1007/s00330-022-09004-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 06/08/2022] [Accepted: 06/30/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES Determining the clinical homogeneous and heterogeneous sets among depressive patients is the key to facilitate individual-level treatment decision. METHODS The diffusion tensor imaging (DTI) data of 62 patients with major depressive disorder (MDD) and 39 healthy controls were used to construct a Latent Dirichlet Allocation (LDA) Bayesian model. Another 48 MDD patients were used to verify the robustness. The LDA model was employed to identify both shared and unique imaging-derived factors of two typically antidepressant-targeted depressive patients, selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs). Furthermore, we applied canonical correlation analysis (CCA) between each factor loading and Hamilton depression rating scale (HAMD) sub-score, to explore the potential neurophysiological significance of each factor. RESULTS The results revealed the imaging-derived connectional fingerprint of all patients could be situated along three latent factor dimensions; such results were also verified by the out-of-sample dataset. Factor 1, uniquely expressed by SNRI-targeted patients, was associated with retardation (r = 0.4, p = 0.037) and characterized by coupling patterns between default mode network and cognitive control network. Factor 3, uniquely expressed by SSRI-targeted patients, was associated with cognitive impairment (r = 0.36, p = 0.047) and characterized by coupling patterns within cognitive control and attention network, and the connectivity between threat and reward network. Shared factor 2, characterized by coupling patterns within default mode network, was associated with anxiety (r = 0.54, p = 0.005) and sleep disturbance (r = 0.37, p = 0.032). CONCLUSIONS Our findings suggested that quantification of both homogeneity and heterogeneity within MDD may have the potential to inform rational design of pharmacological therapies. KEY POINTS • The shared and unique manifestations guiding pharmacotherapy of depressive patients are caused by the homogeneity and heterogeneity of underlying structural connections of the brain. • Both shared and unique factor loadings were found in different antidepressant-targeted patients. • Significant correlations between factor loading and HAMD sub-scores were found.
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Affiliation(s)
- Li Xue
- School of Biological Science & Medical Engineering, Southeast University, No. 2 Sipailou, Nanjing, Jiangsu Province, 210096, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Junneng Shao
- School of Biological Science & Medical Engineering, Southeast University, No. 2 Sipailou, Nanjing, Jiangsu Province, 210096, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Huan Wang
- School of Biological Science & Medical Engineering, Southeast University, No. 2 Sipailou, Nanjing, Jiangsu Province, 210096, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Xinyi Wang
- School of Biological Science & Medical Engineering, Southeast University, No. 2 Sipailou, Nanjing, Jiangsu Province, 210096, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Rongxin Zhu
- 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
| | - 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 Science & Medical Engineering, Southeast University, No. 2 Sipailou, Nanjing, Jiangsu Province, 210096, China. .,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China.
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Bobo WV, Van Ommeren B, Athreya AP. Machine learning, pharmacogenomics, and clinical psychiatry: predicting antidepressant response in patients with major depressive disorder. Expert Rev Clin Pharmacol 2022; 15:927-944. [DOI: 10.1080/17512433.2022.2112949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- William V. Bobo
- Department of Psychiatry & Psychology, Mayo Clinic Florida, Jacksonville, FL, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN & Jacksonville, FL, USA
| | | | - Arjun P. Athreya
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
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Kim JM, Kang HJ, Kim JW, Jhon M, Choi W, Lee JY, Kim SW, Shin IS, Kim MG, Stewart R. Prospective associations of multimodal serum biomarkers with 12-week and 12-month remission in patients with depressive disorders receiving stepwise psychopharmacotherapy. Brain Behav Immun 2022; 104:65-73. [PMID: 35618226 DOI: 10.1016/j.bbi.2022.05.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 05/13/2022] [Accepted: 05/20/2022] [Indexed: 11/26/2022] Open
Abstract
Prognostic biomarkers for depression treatment outcomes have yet to be elucidated. This study sought to evaluate whether a multi-modal serum biomarker panel was prospectively associated with 12-week and 12-month remission in outpatients with depressive disorders receiving stepwise psychopharmacotherapy. At baseline, 14 serum biomarkers and socio-demographic/clinical characteristics were evaluated in 1094 patients. They received initial antidepressant monotherapy followed, as required by a protocol of successive alternative pharmacological strategies administered in 3-week steps during the acute (3-12 week) phase (N = 1086), and in 3-month steps during the continuation (6-12 month) phase (N = 884). Remission was defined as a Hamilton Depression Rating Scale score of ≤ 7. Remission was achieved in 490 (45.1%) over the 12-week, and in 625 (70.7%) over the 12-month, treatment periods. Combination scores of four serum biomarkers (high-sensitivity C-reactive protein, interleukin-1 beta, interleukin-6, and leptin) were prospectively associated with 12-week remission; and four (high-sensitivity C-reactive protein, tumor necrosis factor-alpha, interleukin-1 beta, and brain-derived neurotrophic factor) were prospectively associated with 12-month remission in a clear gradient manner (P-values < 0.001) and after adjustment for relevant covariates. These associations were evident after the Step 1 treatment monotherapy but weakened with increasing treatment steps, falling below statistical significance after 4 + treatment steps. Application of combined multiple serum biomarkers, particularly on inflammatory markers, could improve predictability of remission at acute and continuation treatment phases for depressive disorders. Patients with unfavourable biomarkers might require alternative treatment regimes for better outcomes.
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Affiliation(s)
- Jae-Min Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea.
| | - Hee-Ju Kang
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Ju-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Min Jhon
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Wonsuk Choi
- Department of Internal Medicine, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| | - Ju-Yeon Lee
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Il-Seon Shin
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Min-Gon Kim
- Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Robert Stewart
- Department of Psychological Medicine, King's College London (Institute of Psychiatry, Psychology and Neuroscience), UK; South London and Maudsley NHS Foundation Trust, London, UK.
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40
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Rost N, Brückl TM, Koutsouleris N, Binder EB, Müller-Myhsok B. Creating sparser prediction models of treatment outcome in depression: a proof-of-concept study using simultaneous feature selection and hyperparameter tuning. BMC Med Inform Decis Mak 2022; 22:181. [PMID: 35836174 PMCID: PMC9284749 DOI: 10.1186/s12911-022-01926-2] [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: 04/29/2022] [Accepted: 07/07/2022] [Indexed: 12/03/2022] Open
Abstract
Background Predicting treatment outcome in major depressive disorder (MDD) remains an essential challenge for precision psychiatry. Clinical prediction models (CPMs) based on supervised machine learning have been a promising approach for this endeavor. However, only few CPMs have focused on model sparsity even though sparser models might facilitate the translation into clinical practice and lower the expenses of their application. Methods In this study, we developed a predictive modeling pipeline that combines hyperparameter tuning and recursive feature elimination in a nested cross-validation framework. We applied this pipeline to a real-world clinical data set on MDD treatment response and to a second simulated data set using three different classification algorithms. Performance was evaluated by permutation testing and comparison to a reference pipeline without nested feature selection. Results Across all models, the proposed pipeline led to sparser CPMs compared to the reference pipeline. Except for one comparison, the proposed pipeline resulted in equally or more accurate predictions. For MDD treatment response, balanced accuracy scores ranged between 61 and 71% when models were applied to hold-out validation data. Conclusions The resulting models might be particularly interesting for clinical applications as they could reduce expenses for clinical institutions and stress for patients. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01926-2.
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Affiliation(s)
- Nicolas Rost
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany. .,International Max Planck Research School for Translational Psychiatry, Munich, Germany.
| | - Tanja M Brückl
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University, Munich, Germany.,Max Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College, London, UK
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany
| | - Bertram Müller-Myhsok
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany.,Department of Health Data Science, University of Liverpool, Liverpool, UK
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Biomarkers as predictors of treatment response to tricyclic antidepressants in major depressive disorder: A systematic review. J Psychiatr Res 2022; 150:202-213. [PMID: 35397333 DOI: 10.1016/j.jpsychires.2022.03.057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/14/2022] [Accepted: 03/31/2022] [Indexed: 11/21/2022]
Abstract
Tricyclic antidepressants (TCAs) are frequently prescribed in case of non-response to first-line antidepressants in Major Depressive Disorder (MDD). Treatment of MDD often entails a trial-and-error process of finding a suitable antidepressant and its appropriate dose. Nowadays, a shift is seen towards a more personalized treatment strategy in MDD to increase treatment efficacy. One of these strategies involves the use of biomarkers for the prediction of antidepressant treatment response. We aimed to summarize biomarkers for prediction of TCA specific (i.e. per agent, not for the TCA as a drug class) treatment response in unipolar nonpsychotic MDD. We performed a systematic search in PubMed and MEDLINE. After full-text screening, 36 papers were included. Seven genetic biomarkers were identified for nortriptyline treatment response. For desipramine, we identified two biomarkers; one genetic and one nongenetic. Three nongenetic biomarkers were identified for imipramine. None of these biomarkers were replicated. Quality assessment demonstrated that biomarker studies vary in endpoint definitions and frequently lack power calculations. None of the biomarkers can be confirmed as a predictor for TCA treatment response. Despite the necessity for TCA treatment optimization, biomarker studies reporting drug-specific results for TCAs are limited and adequate replication studies are lacking. Moreover, biomarker studies generally use small sample sizes. To move forward, larger cohorts, pooled data or biomarkers combined with other clinical characteristics should be used to improve predictive power.
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Pilmeyer J, Huijbers W, Lamerichs R, Jansen JFA, Breeuwer M, Zinger S. Functional MRI in major depressive disorder: A review of findings, limitations, and future prospects. J Neuroimaging 2022; 32:582-595. [PMID: 35598083 PMCID: PMC9540243 DOI: 10.1111/jon.13011] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/04/2022] [Accepted: 05/04/2022] [Indexed: 02/02/2023] Open
Abstract
Objective diagnosis and prognosis in major depressive disorder (MDD) remains a challenge due to the absence of biomarkers based on physiological parameters or medical tests. Numerous studies have been conducted to identify functional magnetic resonance imaging‐based biomarkers of depression that either objectively differentiate patients with depression from healthy subjects, predict personalized treatment outcome, or characterize biological subtypes of depression. While there are some findings of consistent functional biomarkers, there is still lack of robust data acquisition and analysis methodology. According to current findings, primarily, the anterior cingulate cortex, prefrontal cortex, and default mode network play a crucial role in MDD. Yet, there are also less consistent results and the involvement of other regions or networks remains ambiguous. We further discuss image acquisition, processing, and analysis limitations that might underlie these inconsistencies. Finally, the current review aims to address and discuss possible remedies and future opportunities that could improve the search for consistent functional imaging biomarkers of depression. Novel acquisition techniques, such as multiband and multiecho imaging, and neural network‐based cleaning approaches can enhance the signal quality in limbic and frontal regions. More comprehensive analyses, such as directed or dynamic functional features or the identification of biological depression subtypes, can improve objective diagnosis or treatment outcome prediction and mitigate the heterogeneity of MDD. Overall, these improvements in functional MRI imaging techniques, processing, and analysis could advance the search for biomarkers and ultimately aid patients with MDD and their treatment course.
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Affiliation(s)
- Jesper Pilmeyer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands
| | - Willem Huijbers
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Philips Research, Eindhoven, The Netherlands
| | - Rolf Lamerichs
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands.,Philips Research, Eindhoven, The Netherlands
| | - Jacobus F A Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands.,School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Marcel Breeuwer
- Philips Healthcare, Best, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Svitlana Zinger
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands
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Zhou Y, Zhang Z, Wang C, Lan X, Li W, Zhang M, Lao G, Wu K, Chen J, Li G, Ning Y. Predictors of 4-week antidepressant outcome in patients with first-episode major depressive disorder: An ROC curve analysis. J Affect Disord 2022; 304:59-65. [PMID: 35172174 DOI: 10.1016/j.jad.2022.02.029] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 02/06/2022] [Accepted: 02/12/2022] [Indexed: 12/28/2022]
Abstract
BACKGROUND Pretreatment characteristics of patients, symptom and function could be associated with antidepressant treatment outcome, but its predictive ability is not adequate. Our study aimed to identify predictors of acute antidepressant efficacy in patients with first-episode Major Depressive Disorder (MDD). METHODS 187 patients with first-episode MDD were included and assessed clinical symptoms, cognitive function and global functioning using the 17-item Hamilton Depression Inventory (HAMD-17), MATRICS Consensus Cognitive Battery (MCCB) and Global Assessment of Functioning (GAF). Participants received treatment with a SSRI (escitalopram or venlafaxine) for 4 weeks. Logistic regression was used to analyze the association between patients' characteristics, symptom profiles, cognitive performance, and global functioning and the antidepressant outcome at the end of 4 weeks, and ROC curve analysis was performed for predictive accuracy with area under the receiver operating curve (AUC). RESULTS Antidepressant improvement, response and remission rate at week 4 was 87.7%, 64.7% and 42.8%, respectively. The combination of pretreatment clinical profiles, speed of processing and global functioning showed moderate discrimination of acute improvement, response and remission with AUCs of 0.863, 0.812 and 0.734, respectively. LIMITATIONS The major limitation of the present study is the study did not combine pharmacogenomics from the perspective of antidepressant drug metabolism. CONCLUSION Aside from the baseline clinical symptoms, cognitive function and global functioning could be predictors of acute treatment outcome in first episode MDD using escitalopram or venlafaxine. This relatively simple application based on clinical symptoms and function seems to be cost-effective method to identify individuals who are more likely to respond to antidepressant treatment.
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Affiliation(s)
- Yanling Zhou
- Department of Psychiatry, Department of Neurology, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China
| | - Zhipei Zhang
- Department of Psychiatry, Department of Neurology, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China; Southern Medical University, Guangzhou, China
| | - ChengYu Wang
- Department of Psychiatry, Department of Neurology, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China
| | - Xiaofeng Lan
- Department of Psychiatry, Department of Neurology, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China
| | - Weicheng Li
- Department of Psychiatry, Department of Neurology, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China; Southern Medical University, Guangzhou, China
| | - Muqin Zhang
- Department of Psychiatry, Department of Neurology, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China
| | - Guohui Lao
- Department of Psychiatry, Department of Neurology, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China
| | - Kai Wu
- Department of Psychiatry, Department of Neurology, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China; School of Biomedical Sciences and Engineering, South china University of Technology, Guangzhou, China
| | - Jun Chen
- Guangdong Institute of Medical Instruments, Guangzhou, China
| | - Guixiang Li
- Guangdong Institute of Medical Instruments, Guangzhou, China
| | - Yuping Ning
- Department of Psychiatry, Department of Neurology, Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China; Southern Medical University, Guangzhou, China.
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Pain O, Hodgson K, Trubetskoy V, Ripke S, Marshe VS, Adams MJ, Byrne EM, Campos AI, Carrillo-Roa T, Cattaneo A, Als TD, Souery D, Dernovsek MZ, Fabbri C, Hayward C, Henigsberg N, Hauser J, Kennedy JL, Lenze EJ, Lewis G, Müller DJ, Martin NG, Mulsant BH, Mors O, Perroud N, Porteous DJ, Rentería ME, Reynolds CF, Rietschel M, Uher R, Wigmore EM, Maier W, Wray NR, Aitchison KJ, Arolt V, Baune BT, Biernacka JM, Bondolfi G, Domschke K, Kato M, Li QS, Liu YL, Serretti A, Tsai SJ, Turecki G, Weinshilboum R, McIntosh AM, Lewis CM, Kasper S, Zohar J, Souery D, Montgomery S, Albani D, Forloni G, Ferentinos P, Rujescu D, Mendlewicz J, Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, Adams MJ, Agerbo E, Air TM, Andlauer TF, Bacanu SA, Bækvad-Hansen M, Beekman AT, Bigdeli TB, Binder EB, Bryois J, Buttenschøn HN, Bybjerg-Grauholm J, Cai N, Castelao E, Christensen JH, Clarke TK, Coleman JR, Colodro-Conde L, Couvy-Duchesne B, Craddock N, Crawford GE, Davies G, Deary IJ, Degenhardt F, Derks EM, Direk N, Dolan CV, Dunn EC, Eley TC, Escott-Price V, Hassan Kiadeh FF, Finucane HK, Foo JC, Forstner AJ, Frank J, Gaspar HA, Gill M, Goes FS, Gordon SD, Grove J, Hall LS, Hansen CS, Hansen TF, Herms S, Hickie IB, Hoffmann P, Homuth G, Horn C, Hottenga JJ, Hougaard DM, Howard DM, Ising M, Jansen R, Jones I, Jones LA, Jorgenson E, Knowles JA, Kohane IS, Kraft J, Kretzschmar WW, Kutalik Z, Li Y, Lind PA, MacIntyre DJ, MacKinnon DF, Maier RM, Maier W, Marchini J, Mbarek H, McGrath P, McGuffin P, Medland SE, Mehta D, Middeldorp CM, Mihailov E, Milaneschi Y, Milani L, Mondimore FM, Montgomery GW, Mostafavi S, Mullins N, Nauck M, Ng B, Nivard MG, Nyholt DR, O’Reilly PF, Oskarsson H, Owen MJ, Painter JN, Pedersen CB, Pedersen MG, Peterson RE, Peyrot WJ, Pistis G, Posthuma D, Quiroz JA, Qvist P, Rice JP, Riley BP, Rivera M, Mirza SS, Schoevers R, Schulte EC, Shen L, Shi J, Shyn SI, Sigurdsson E, Sinnamon GC, Smit JH, Smith DJ, Stefansson H, Steinberg S, Streit F, Strohmaier J, Tansey KE, Teismann H, Teumer A, Thompson W, Thomson PA, Thorgeirsson TE, Traylor M, Treutlein J, Trubetskoy V, Uitterlinden AG, Umbricht D, Van der Auwera S, van Hemert AM, Viktorin A, Visscher PM, Wang Y, Webb BT, Weinsheimer SM, Wellmann J, Willemsen G, Witt SH, Wu Y, Xi HS, Yang J, Zhang F, Arolt V, Baune BT, Berger K, Boomsma DI, Cichon S, Dannlowski U, de Geus E, DePaulo JR, Domenici E, Domschke K, Esko T, Grabe HJ, Hamilton SP, Hayward C, Heath AC, Kendler KS, Kloiber S, Lewis G, Li QS, Lucae S, Madden PA, Magnusson PK, Martin NG, McIntosh AM, Metspalu A, Mors O, Mortensen PB, Müller-Myhsok B, Nordentoft M, Nöthen MM, O’Donovan MC, Paciga SA, Pedersen NL, Penninx BW, Perlis RH, Porteous DJ, Potash JB, Preisig M, Rietschel M, Schaefer C, Schulze TG, Smoller JW, Stefansson K, Tiemeier H, Uher R, Völzke H, Weissman MM, Werge T, Lewis CM, Levinson DF, Breen G, Børglum AD, Sullivan PF. Identifying the Common Genetic Basis of Antidepressant Response. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022; 2:115-126. [PMID: 35712048 PMCID: PMC9117153 DOI: 10.1016/j.bpsgos.2021.07.008] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 01/20/2023] Open
Abstract
Background Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction. Methods Genome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA. Results Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response. Conclusions This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.
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Qiao D, Liu H, Zhang X, Lei L, Sun N, Yang C, Li G, Guo M, Zhang Y, Zhang K, Liu Z. Exploring the potential of thyroid hormones to predict clinical improvements in depressive patients: A machine learning analysis of the real-world based study. J Affect Disord 2022; 299:159-165. [PMID: 34856305 DOI: 10.1016/j.jad.2021.11.055] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/17/2021] [Accepted: 11/19/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Although undergoing antidepressant treatments, many patients continue to struggle with chronic depression episode. Seeking the potential biomarkers and establishing a predictive model of clinical improvements is vital to optimize personalized management of depression. Mounting evidence showed thyroid hormones changes are central to leading paradigms of depression. METHODS Here, we conducted a real-world based retrospective study using clinical and biochemical data of 2086 depressive inpatients during period of 2014-2020. We first performed regression analyses to evaluate the contributing effect of free triiodothyronine (FT3), free thyroxine (FT4) and thyroid stimulating hormone (TSH) in predicting the clinical outcomes of depression. Then we established 7 predictive models using different combination of such hormones by supervised learning methods and tested the actual prediction efficacy on clinical outcomes, in order to select the one with the best predictive power. RESULTS The results showed that lower values of FT3 and FT4 can both predict a poor clinical outcome in depression. Further, a model with the best performance was selected (sensitivity=0.91, specificity=0.79, and ROC-AUC=0.86), including the values of FT3 and FT4, and the scores of Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA) as features. LIMITATIONS The predictive model requires further external validation, and multi-center researches to confirm its clinical applicability. CONCLUSIONS Our findings present a crucial role of thyroid measurements in predicting clinical outcomes of depression. Assessment of thyroid hormone should be extended to routine practice settings to determine which patients should be most in need of earlier or intensive interventions for preventing continued dysfunction.
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Affiliation(s)
- Dan Qiao
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Huishan Liu
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Xuemin Zhang
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Lei Lei
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Ning Sun
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Chunxia Yang
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Gaizhi Li
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Meng Guo
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Yu Zhang
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Kerang Zhang
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China.
| | - Zhifen Liu
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan, 030001, China.
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Yang J, Zhou J, Zhou J, Wang H, Sun Z, Zhu X, He Y, Wong AHC, Liu F, Wang G. Serum amyloid P component level is associated with clinical response to escitalopram treatment in patients with major depressive disorder. J Psychiatr Res 2022; 146:172-178. [PMID: 34995992 DOI: 10.1016/j.jpsychires.2021.12.051] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/04/2021] [Accepted: 12/21/2021] [Indexed: 12/26/2022]
Abstract
Serum amyloid P component (SAP) is a universal constituent of human amyloid deposits, which has been implicated in Alzheimer's disease and major depressive disorder (MDD). However, the relationship between SAP level and depression severity remains obscure. The aims of this study were to investigate how SAP is involved in depression and to explore the association between SAP level and antidepressant treatment response. Patients with MDD (n = 85) who received escitalopram monotherapy for 8-12 weeks were selected from a multicenter open-label randomized clinical trial. The same number of healthy controls was recruited. Depression severity was measured according to the Hamilton Depression Rating Scale (HAMD-17) at baseline and weeks 4, 8, and 12. The plasma levels of SAP were measured at baseline, week 2 and week 12. As a result, baseline levels of SAP were significantly higher in depressed patients than in control subjects (p < 0.001). SAP levels at baseline were negatively associated with depression severity after escitalopram treatment (p < 0.05), and the changes in SAP levels from baseline to week 12 were highly correlated with the severity of depressive symptoms based on the HAMD-17 score (p < 0.05). Interestingly, treatment with escitalopram significantly decreased the plasma levels of SAP in females, but not in males. Altogether, our results suggest that SAP not only involved in the pathobiology of depression but also mediates the action of antidepressant medications.
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Affiliation(s)
- Jian Yang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China, Beijing, China
| | - Jingjing Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China, Beijing, China
| | - Jia Zhou
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Haixia Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Zuoli Sun
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Xuequan Zhu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Yi He
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Albert H C Wong
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Fang Liu
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China, Beijing, China; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
| | - Gang Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China, Beijing, China.
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Evaluating the efficacy and moderators of algorithm-guided antidepressant treatments of major depressive disorder. J Affect Disord 2022; 297:68-75. [PMID: 34670132 DOI: 10.1016/j.jad.2021.10.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/03/2021] [Accepted: 10/11/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND In spite of numerous options, the most efficacious treatment for major depressive disorder (MDD) remains elusive. Algorithm-guided treatments (AGTs) are proposed to address inadequate remission and optimize treatment delivery. This study aimed to evaluate the clinical benefit of AGTs for MDD, and to explore specific moderators of treatment outcomes for individual patients. METHODS The study recruited 987 patients with MDD across eight hospitals who were randomly assigned to AGT with escitalopram (AGT-E), AGT with mirtazapine (AGT-M), or treatment-as-usual (TAU). The outcomes were symptom remission, response rate, early improvement rate, subsymptom clusters improvement over time, the mean time to first remission, relapse rate at 6-months posttreatment follow-up, quality of life (QOL), and adverse events. RESUTLS No significant differences were observed across groups in outcome, except that TAU showed significantly poorer QOL, higher relapse rates at 6-months posttreatment follow-up, and marginally significantly worse maximal burden of adverse events than the AGT groups. After 6 weeks of treatment initiation, remission rate did not significantly increase with extended treatment. AGT-M outperformed the TAU and AGT-E in treating sleep symptoms. AGT-E was less effective than AGT-M and TAU in patients with severe depression and somatic symptoms (DSSS). The superiority of TAU over AGTs was observed in recurrent MDD patients. CONCLUSION Although the superiority of AGTs over TAU was limited by failure of alternative subsequent treatment, AGTs outperformed in QOL and relapse rate. Types of disease episode and DSSS were regarded as specific moderators in treatment of depression. These findings might contribute to future research on targeted antidepressant treatment.
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Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2021.12.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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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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