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Bartova L, Fugger G, Dold M, Kautzky A, Bairhuber I, Kloimstein P, Fanelli G, Zanardi R, Weidenauer A, Rujescu D, Souery D, Mendlewicz J, Zohar J, Montgomery S, Fabbri C, Serretti A, Kasper S. The clinical perspective on late-onset depression in European real-world treatment settings. Eur Neuropsychopharmacol 2024; 84:59-68. [PMID: 38678879 DOI: 10.1016/j.euroneuro.2024.03.007] [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: 11/20/2023] [Revised: 03/17/2024] [Accepted: 03/18/2024] [Indexed: 05/01/2024]
Abstract
The clinical phenotype of the so-called late-onset depression (LOD) affecting up to 30% of older adults and yielding heterogeneous manifestations concerning symptoms, severity and course has not been fully elucidated yet. This European, cross-sectional, non-interventional, naturalistic multicenter study systematically investigated socio-demographic and clinical correlates of early-onset depression (EOD) and LOD (age of onset ≥ 50 years) in 1410 adult in- and outpatients of both sexes receiving adequate psychopharmacotherapy. In a total of 1329 patients (94.3%) with known age of disease onset, LOD was identified in 23.2% and was associated with unemployment, an ongoing relationship, single major depressive episodes, lower current suicidal risk and higher occurrence of comorbid hypertension. In contrast, EOD was related to higher rates of comorbid migraine and additional psychotherapy. Although the applied study design does not allow to draw any causal conclusions, the present results reflect broad clinical settings and emphasize easily obtainable features which might be characteristic for EOD and LOD. A thoughtful consideration of age of onset might, hence, contribute to optimized diagnostic and therapeutic processes in terms of the globally intended precision medicine, ideally enabling early and adequate treatment allocations and implementation of respective prevention programs.
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Affiliation(s)
- Lucie Bartova
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Gernot Fugger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria; Psychiatric Day Hospital University Hospital St. Poelten, Karl Landsteiner Private University of Health Sciences, Krems an der Donau, Austria
| | - Markus Dold
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Alexander Kautzky
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Isabella Bairhuber
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Philipp Kloimstein
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria; Center for Addiction Medicine, Foundation Maria Ebene, Frastanz, Austria
| | - Giuseppe Fanelli
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands
| | - Raffaella Zanardi
- Vita-Salute San Raffaele University, Milano, Italy; Mood Disorders Unit, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy
| | - Ana Weidenauer
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Dan Rujescu
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Daniel Souery
- PsyPluriel - Outpatient Department EPSYLON asbl - Epsylon Caring from Mental Health Brussels, Brussels, Belgium
| | | | - Joseph Zohar
- Psychiatric Division, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | - Stuart Montgomery
- Imperial College School of Medicine, University of London, London, United Kingdom
| | - Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Department of Medicine and Surgery, Kore University of Enna, Enna, Italy
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria; Center for Brain Research, Medical University of Vienna, Vienna, Austria.
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Vittengl JR, Jarrett RB, Ro E, Clark LA. Evaluating a Comprehensive Model of Euthymia. PSYCHOTHERAPY AND PSYCHOSOMATICS 2023; 92:133-138. [PMID: 36917971 PMCID: PMC10871685 DOI: 10.1159/000529784] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/15/2023] [Indexed: 03/16/2023]
Abstract
INTRODUCTION In research and treatment of mood disorders, "euthymia" traditionally denotes the absence of clinically significant mood disturbance. A newer, expanded definition of euthymia also includes positive affect and psychological well-being. OBJECTIVE We aimed to test this comprehensive model of euthymia and estimate the coherence and predictive power of each factor in the model. METHODS Community-dwelling adults (N = 601), including both mental health outpatients and non-patients at high risk for personality pathology, completed a battery of interviews and questionnaires at time 1. Most (n = 497) were reassessed on average 8 months later (time 2). We modeled euthymia using standard mood, personality, and psychosocial functioning assessments rather than measures designed specifically for euthymia. RESULTS The hypothesized model of euthymia was supported by confirmatory factor analysis: specific measures loaded on three lower order factors (mood disturbance, positive affect, and psychological well-being) that reflected general euthymia at time 1. Each factor (general euthymia plus lower order factors) demonstrated moderately strong concurrent (time 1) and predictive (time 1-2) correlations with outcomes, including employment status, income, mental health treatment consumption, and disability. Compared to positive affect and psychological well-being, mood disturbance had stronger incremental (i.e., nonoverlapping) relations with these outcomes. CONCLUSIONS Support for a comprehensive model of euthymia reinforces efforts to improve assessment and treatment of mood and other disorders. Beyond dampening of psychological distress, euthymia-informed treatment goals encompass full recovery, including enjoyment and meaning in life.
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Affiliation(s)
| | - Robin B. Jarrett
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Eunyoe Ro
- Department of Psychology, Southern Illinois University Edwardsville, Edwardsville, IL, USA
| | - Lee Anna Clark
- Department of Psychology, Notre Dame University, Notre Dame, IN, USA
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Abstract
PURPOSE OF REVIEW The present review aims to examine, summarize and update information on the sociodemographic and cultural determinants of mood disorders. RECENT FINDINGS Known sociodemographic and cultural determinants continue to be good predictors of the risk of developing a mood disorder over the lifetime. Polygenic risk scores do not appear to offer any advantages over these determinants at present. There is also new and emerging understanding of the role of lifestyle and environmental factors in mediating vulnerability to mood disorder. The influence of ethnicity and migration, on the other hand, is far more complex than initially envisaged. SUMMARY Recent evidence on sociodemographic determinants of mood disorders confirms associations derived from existing literature. There is also new and emerging evidence on how quality of sleep, diet and the environment influence risk of mood disorders. Culture and ethnicity, depending on context, may contribute to both vulnerability and resilience. Socioeconomic deprivation may be the final common pathway through which several sociodemographic and cultural determinants of mood disorders act.
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Affiliation(s)
- Migita M D'cruz
- DM Geriatric Psychiatry, Consultant, Geriatric Psychiatry, Kollam, Kerala, India
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Dold M, Bartova L, Fugger G, Mitschek MM, Fabbri C, Serretti A, Mendlewicz J, Souery D, Zohar J, Montgomery S, Kasper S. Pregabalin augmentation of antidepressants in major depression - results from a European multicenter study. J Affect Disord 2022; 296:485-492. [PMID: 34653701 DOI: 10.1016/j.jad.2021.09.063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/12/2021] [Accepted: 09/21/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND We aimed to investigate the prescription pattern of pregabalin augmentation of antidepressants in major depressive disorder (MDD) and to explore variables associated with add-on pregabalin treatment. METHODS 1410 MDD patients participated in this naturalistic European multicenter study with retrospective assessment of treatment response. Analyses of covariance, chi-squared tests, and binary logistic regressions were accomplished to determine differences in socio-demographic and clinical characteristics between MDD patients with and without pregabalin augmentation. RESULTS Add-on pregabalin was established in 102 (7.23%) MDD patients. Compared to those without receiving pregabalin, pregabalin-treated patients were characterized by a significantly higher likelihood for older age (mean: 54.74 ± 13.08 vs 49.93 ± 14.13 years), unemployment (78.43% vs 51.23%), melancholic features (83.33% vs 58.94%), inpatient treatment (72.55% vs 31.65%), previous psychiatric hospitalizations (13.52 ± 24.82 vs 4.96 ± 19.93 weeks), any somatic comorbidity (68.63% vs 44.57%), comorbid hypertension (37.25% vs 17.51%), more severe depressive symptom severity at the onset of the current episode (mean MADRS: 37.55 ± 9.00 vs 33.79 ± 7.52), receiving augmentation/combination treatment strategies in general (mean number of psychotropic drugs: 3.64 ± 0.92 vs 2.07 ± 1.17), and with antidepressants (50.00% vs 27.91%) and antipsychotics (46.08% vs 24.08%) in particular. LIMITATIONS Due to its observational cross-sectional study design, our patient sample might not be fully representative for MDD patients in primary care settings. CONCLUSIONS Our findings suggest that add-on pregabalin is particularly administered in more severe/difficult-to-treat MDD conditions, whereas no association between the prescription of adjunctive pregabalin and comorbid anxiety symptoms could be determined.
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Affiliation(s)
- Markus Dold
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
| | - Lucie Bartova
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Gernot Fugger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Marleen Mm Mitschek
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | | | - Daniel Souery
- School of Medicine, Free University of Brussels, Brussels, Belgium; Psy Pluriel - European Centre of Psychological Medicine, Brussels, Belgium
| | - Joseph Zohar
- Psychiatric Division, Chaim Sheba Medical Center, Tel Hashomer, Israel
| | | | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria; Center for Brain Research, Medical University of Vienna, Spitalgasse 23, Vienna A-1090, Austria.
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Lin E, Kuo PH, Lin WY, Liu YL, Yang AC, Tsai SJ. Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach. J Pers Med 2021; 11:597. [PMID: 34202750 PMCID: PMC8308113 DOI: 10.3390/jpm11070597] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/14/2021] [Accepted: 06/22/2021] [Indexed: 12/16/2022] Open
Abstract
In light of recent advancements in machine learning, personalized medicine using predictive algorithms serves as an essential paradigmatic methodology. Our goal was to explore an integrated machine learning and genome-wide analysis approach which targets the prediction of probable major depressive disorder (MDD) using 9828 individuals in the Taiwan Biobank. In our analysis, we reported a genome-wide significant association with probable MDD that has not been previously identified: FBN1 on chromosome 15. Furthermore, we pinpointed 17 single nucleotide polymorphisms (SNPs) which show evidence of both associations with probable MDD and potential roles as expression quantitative trait loci (eQTLs). To predict the status of probable MDD, we established prediction models with random undersampling and synthetic minority oversampling using 17 eQTL SNPs and eight clinical variables. We utilized five state-of-the-art models: logistic ridge regression, support vector machine, C4.5 decision tree, LogitBoost, and random forests. Our data revealed that random forests had the highest performance (area under curve = 0.8905 ± 0.0088; repeated 10-fold cross-validation) among the predictive algorithms to infer complex correlations between biomarkers and probable MDD. Our study suggests that an integrated machine learning and genome-wide analysis approach may offer an advantageous method to establish bioinformatics tools for discriminating MDD patients from healthy controls.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Po-Hsiu Kuo
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10617, Taiwan; (P.-H.K.); (W.-Y.L.)
| | - Wan-Yu Lin
- Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10617, Taiwan; (P.-H.K.); (W.-Y.L.)
| | - Yu-Li Liu
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli County 35053, Taiwan;
| | - Albert C. Yang
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA 02215, USA;
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei 11217, Taiwan
- Division of Psychiatry, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
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