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Seemüller F, Schennach R, Musil R, Obermeier M, Adli M, Bauer M, Brieger P, Laux G, Gaebel W, Falkai P, Riedel M, Möller HJ. A factor analytic comparison of three commonly used depression scales (HAMD, MADRS, BDI) in a large sample of depressed inpatients. BMC Psychiatry 2023; 23:548. [PMID: 37507656 PMCID: PMC10386606 DOI: 10.1186/s12888-023-05038-7] [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: 05/14/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Quantifying depression mainly relies on the use of depression scales, and understanding their factor structure is crucial for evaluating their validity. METHODS This post-hoc analysis utilized prospectively collected data from a naturalistic study of 1014 inpatients with major depression. Confirmatory and exploratory factor analyses were performed to test the psychometric abilities of the Hamilton Depression Rating Scale, the Montgomery Asberg Depression Rating Scale, and the self-rated Beck Depression Inventory. A combined factor analysis was also conducted including all items of all scales. RESULTS All three scales showed good to very good internal consistency. The HAMD-17 had four factors: an "anxiety" factor, a "depression" factor, an "insomnia" factor, and a "somatic" factor. The MADRS also had four factors: a "sadness" factor, a neurovegetative factor, a "detachment" factor and a "negative thoughts" factor, while the BDI had three factors: a "negative attitude towards self" factor, a "performance impairment" factor, and a "somatic" factor. The combined factor analysis suggested that self-ratings might reflect a distinct illness dimension within major depression. CONCLUSIONS The factors obtained in this study are comparable to those found in previous research. Self and clinician ratings are complementary and not redundant, highlighting the importance of using multiple measures to quantify depression.
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Affiliation(s)
- Florian Seemüller
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University, Nussbaumstrasse 7, 80336, Munich, Germany.
- Department of Psychiatry, Psychotherapy and Psychosomatics, Kbo-Lech-Mangfall-Klinik, Auenstrasse 6, 82467, Garmisch-Partenkirchen, Germany.
| | - Rebecca Schennach
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University, Nussbaumstrasse 7, 80336, Munich, Germany
- Schoen Clinic Roseneck, Am Roseneck 6, 83209, Prien am Chiemsee, Germany
| | - Richard Musil
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University, Nussbaumstrasse 7, 80336, Munich, Germany
| | - Michael Obermeier
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University, Nussbaumstrasse 7, 80336, Munich, Germany
| | - Mazda Adli
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Charité Mitte (CCM), CampusCharitéplatz 1, 10117, Berlin, Germany
- Center for Psychiatry, Psychotherapy and Psychosomatic Medicine, Fliedner Klinik Berlin, Markgrafenstrasse 34, 10117, Berlin, Germany
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital Dresden, Technische Universität Dresden, Fetscherstrasse 74, 01307, Dresden, Germany
| | - Peter Brieger
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University, Nussbaumstrasse 7, 80336, Munich, Germany
- Department of Psychiatry and Psychotherapy, Kbo-Isar-Amper-Klinikum Region Munich, Vockestrasse 72, 85540, Haar, Germany
| | - Gerd Laux
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University, Nussbaumstrasse 7, 80336, Munich, Germany
- Institute of Psychological Medicine (IPM), Nussbaumstrasse 9, 83564, Soyen, Germany
| | - Wolfgang Gaebel
- Department of Psychiatry and Psychotherapy, University of Düsseldorf, Bergische Landstrasse 2, 40629, Düsseldorf, Germany
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University, Nussbaumstrasse 7, 80336, Munich, Germany
| | - Michael Riedel
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University, Nussbaumstrasse 7, 80336, Munich, Germany
- Centre for Disturbance of Memory and Demetia, Marion von Tessin Memory-Centre, Nymphenburgerstrasse 45, 80636, Munich, Germany
| | - Hans-Jürgen Möller
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University, Nussbaumstrasse 7, 80336, Munich, Germany
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Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome. Diagnostics (Basel) 2021; 11:diagnostics11091631. [PMID: 34573974 PMCID: PMC8468112 DOI: 10.3390/diagnostics11091631] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/03/2021] [Accepted: 09/03/2021] [Indexed: 12/30/2022] Open
Abstract
The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.
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Rodgers S, Calabrese P, Ajdacic-Gross V, Steinemann N, Kaufmann M, Salmen A, Manjaly ZM, Kesselring J, Kamm CP, Kuhle J, Chan A, Gobbi C, Zecca C, Müller S, von Wyl V. Major depressive disorder subtypes and depression symptoms in multiple sclerosis: What is different compared to the general population? J Psychosom Res 2021; 144:110402. [PMID: 33631437 DOI: 10.1016/j.jpsychores.2021.110402] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 01/04/2021] [Accepted: 02/13/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To compare and characterize major depressive disorder (MDD) subtypes (i.e., pure atypical, pure melancholic and mixed atypical-melancholic) and depression symptoms in persons with multiple sclerosis (PwMS) with persons without MS (Pw/oMS) fulfilling the DSM-5 criteria for a past 12-month MDD. METHODS MDD in PwMS (n = 92) from the Swiss Multiple Sclerosis Registry was compared with Pw/oMS (n = 277) from a Swiss community-based study. Epidemiological MDD diagnoses were based on the Mini-SPIKE (shortened form of the Structured Psychopathological Interview and Rating of the Social Consequences for Epidemiology). Logistic and multinomial regression analyses (adjusted for sex, age, civil status, depression and severity) were computed for comparisons and characterization. Latent class analysis (LCA) was conducted to empirically identify depression subtypes in PwMS. RESULTS PwMS had a higher risk for the mixed atypical-melancholic MDD subtype (OR = 2.22, 95% CI = 1.03-4.80) compared to Pw/oMS. MDD in PwMS was specifically characterized by a higher risk of the two somatic atypical depression symptoms 'weight gain' (OR = 6.91, 95% CI = 2.20-21.70) and 'leaden paralysis' (OR = 3.03, 95% CI = 1.35-6.82) and the symptom 'irritable/angry' (OR = 3.18, 95% CI = 1.08-9.39). CONCLUSIONS MDD in PwMS was characterized by a higher risk for specific somatic atypical depression symptoms and the mixed atypical-melancholic MDD subtype. The pure atypical MDD subtype, however, did not differentiate between PwMS and Pw/oMS. Given the high phenomenological overlap with MS symptoms, the mixed atypical-melancholic MDD subtype represents a particular diagnostic challenge.
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Affiliation(s)
- Stephanie Rodgers
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich (UZH), Zurich, Switzerland.
| | - Pasquale Calabrese
- Division of Molecular and Cognitive Neuroscience, University of Basel, Basel, Switzerland
| | - Vladeta Ajdacic-Gross
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich (UZH), Zurich, Switzerland; Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich (PUK), Zurich, Switzerland
| | - Nina Steinemann
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich (UZH), Zurich, Switzerland
| | - Marco Kaufmann
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich (UZH), Zurich, Switzerland
| | - Anke Salmen
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Zina-Mary Manjaly
- Department of Neurology, Schulthess Clinic, Zürich, Switzerland; Department of Health Sciences and Technology, ETH Zurich, Zürich, Switzerland
| | - Jürg Kesselring
- Department of Neurology and Neurorehabilitation, Rehabilitation Centre Kliniken Valens, Valens, Switzerland
| | - Christian P Kamm
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland; Neurocentre, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, Departments of Medicine, Biomedicine and Clinical Research, University Hospital and University of Basel, Basel, Switzerland
| | - Andrew Chan
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Claudio Gobbi
- Department of Neurology, Multiple Sclerosis Center (MSC), Neurocenter of Southern Switzerland, 6900 Lugano, Switzerland; Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), 6900 Lugano, Switzerland
| | - Chiara Zecca
- Department of Neurology, Multiple Sclerosis Center (MSC), Neurocenter of Southern Switzerland, 6900 Lugano, Switzerland; Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), 6900 Lugano, Switzerland
| | - Stefanie Müller
- Department of Neurology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Viktor von Wyl
- Epidemiology, Biostatistics and Prevention Institute, University of Zurich (UZH), Zurich, Switzerland
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Kautzky A, Möller H, Dold M, Bartova L, Seemüller F, Laux G, Riedel M, Gaebel W, Kasper S. Combining machine learning algorithms for prediction of antidepressant treatment response. Acta Psychiatr Scand 2021; 143:36-49. [PMID: 33141944 PMCID: PMC7839691 DOI: 10.1111/acps.13250] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 06/29/2020] [Accepted: 10/12/2020] [Indexed: 12/13/2022]
Abstract
OBJECTIVES Predictors for unfavorable treatment outcome in major depressive disorder (MDD) applicable for treatment selection are still lacking. The database of a longitudinal multicenter study on 1079 acutely depressed patients, performed by the German research network on depression (GRND), allows supervised and unsupervised learning to further elucidate the interplay of clinical and psycho-sociodemographic variables and their predictive impact on treatment outcome phenotypes. EXPERIMENTAL PROCEDURES Treatment response was defined by a change of HAM-D 17-item baseline score ≥50% and remission by the established threshold of ≤7, respectively, after up to eight weeks of inpatient treatment. After hierarchical symptom clustering and stratification by treatment subtypes (serotonin reuptake inhibitors, tricyclic antidepressants, antipsychotic, and lithium augmentation), prediction models for different outcome phenotypes were computed with random forest in a cross-center validation design. In total, 88 predictors were implemented. RESULTS Clustering revealed four distinct HAM-D subscores related to emotional, anxious, sleep, and appetite symptoms, respectively. After feature selection, classification models reached moderate to high accuracies up to 0.85. Highest accuracies were observed for the SSRI and TCA subgroups and for sleep and appetite symptoms, while anxious symptoms showed poor predictability. CONCLUSION Our results support a decisive role for machine learning in the management of antidepressant treatment. Treatment- and symptom-specific algorithms may increase accuracies by reducing heterogeneity. Especially, predictors related to duration of illness, baseline depression severity, anxiety and somatic symptoms, and personality traits moderate treatment success. However, prospectives application of machine learning models will be necessary to prove their value for the clinic.
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Affiliation(s)
- Alexander Kautzky
- Department of Psychiatry and PsychotherapyMedical University of ViennaViennaAustria
| | - Hans‐Juergen Möller
- Department of Psychiatry and PsychotherapyLudwig‐Maximilians‐Q3 University MunichMunichGermany
| | - Markus Dold
- Department of Psychiatry and PsychotherapyMedical University of ViennaViennaAustria
| | - Lucie Bartova
- Department of Psychiatry and PsychotherapyMedical University of ViennaViennaAustria
| | - Florian Seemüller
- Department of Psychiatry and PsychotherapyLudwig‐Maximilians‐Q3 University MunichMunichGermany,Department of Psychiatry and Psychotherapykbo‐Lech‐Mangfall‐KlinikGarmisch‐PartenkirchenGermany
| | - Gerd Laux
- Department of Psychiatry and Psychotherapykbo‐Inn‐Salzach‐KlinikumWasserburgGermany
| | - Michael Riedel
- Department of Psychiatry and PsychotherapyLudwig‐Maximilians‐Q3 University MunichMunichGermany,Department of PsychiatrySächsisches KrankenhausRodewischGermany
| | - Wolfgang Gaebel
- Department of Psychiatry and PsychotherapyMedical FacultyHeinrich‐Heine‐UniversityDüsseldorfGermany
| | - Siegfried Kasper
- Department of Psychiatry and PsychotherapyMedical University of ViennaViennaAustria
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Möller HJ, Bitter I, Bobes J, Fountoulakis K, Höschl C, Kasper S. Position statement of the European Psychiatric Association (EPA) on the value of antidepressants in the treatment of unipolar depression. Eur Psychiatry 2020; 27:114-28. [DOI: 10.1016/j.eurpsy.2011.08.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2011] [Revised: 08/24/2011] [Accepted: 08/25/2011] [Indexed: 12/28/2022] Open
Abstract
AbstractThis position statement will address in an evidence-based approach some of the important issues and controversies of current drug treatment of depression such as the efficacy of antidepressants, their effect on suicidality and their place in a complex psychiatric treatment strategy including psychotherapy. The efficacy of antidepressants is clinically relevant. The highest effect size was demonstrated for severe depression. Based on responder rates and based on double-blind placebo-controlled studies, the number needed to treat (NNT) is 5–7 for acute treatment and four for maintenance treatment. Monotherapy with one drug is often not sufficient and has to be followed by other antidepressants or by comedication/augmentation therapy approaches. Generally, antidepressants reduce suicidality, but under special conditions like young age or personality disorder, they can also increase suicidality. However, under the conditions of good clinical practice, the risk–benefit relationship of treatment with antidepressants can be judged as favourable also in this respect. The capacity of psychiatrists to individualise and optimise treatment decisions in terms of ‘the right drug/treatment for the right patient’ is still restricted since currently there are no sufficient powerful clinical or biological predictors which could help to achieve this goal. There is hope that in future pharmacogenetics will contribute significantly to a personalised treatment. With regard to plasma concentration, therapeutic drug monitoring (TDM) is a useful tool to optimize plasma levels therapeutic outcome. The ideal that all steps of clinical decision-making can be based on the strict rules of evidence-based medicine is far away from reality. Clinical experience so far still has a great impact.
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6
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Schennach R, Riedel M, Obermeier M, Seemüller F, Jäger M, Schmauss M, Laux G, Pfeiffer H, Naber D, Schmidt L, Gaebel W, Klosterkötter J, Heuser I, Maier W, Lemke M, Rüther E, Klingberg S, Gastpar M, Möller HJ. What are depressive symptoms in acutely ill patients with schizophrenia spectrum disorder? Eur Psychiatry 2020; 30:43-50. [DOI: 10.1016/j.eurpsy.2014.11.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2014] [Revised: 11/01/2014] [Accepted: 11/03/2014] [Indexed: 10/24/2022] Open
Abstract
AbstractBackground:Aim was to examine depressive symptoms in acutely ill schizophrenia patients on a single symptom basis and to evaluate their relationship with positive, negative and general psychopathological symptoms.Methods:Two hundred and seventy-eight patients suffering from a schizophrenia spectrum disorder were analysed within a naturalistic study by the German Research Network on Schizophrenia. Using the Calgary Depression Scale for Schizophrenia (CDSS) depressive symptoms were examined and the Positive and Negative Syndrome Scale (PANSS) was applied to assess positive, negative and general symptoms. Correlation and factor analyses were calculated to detect the underlying structure and relationship of the patient’s symptoms.Results:The most prevalent depressive symptoms identified were depressed mood (80%), observed depression (62%) and hopelessness (54%). Thirty-nine percent of the patients suffered from depressive symptoms when applying the recommended cut-off of a CDSS total score of > 6 points at admission. Negligible correlations were found between depressive and positive symptoms as well as most PANSS negative and global symptoms despite items on depression, guilt and social withdrawal. The factor analysis revealed that the factor loading with the PANSS negative items accounted for most of the data variance followed by a factor with positive symptoms and three depression-associated factors.Limitations:The naturalistic study design does not allow a sufficient control of study results for the effect of different pharmacological treatments possibly influencing the appearance of depressive symptoms.Conclusion:Results suggest that depressive symptoms measured with the CDSS are a discrete symptom domain with only partial overlap with positive or negative symptoms.
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Xin LM, Chen L, Su YA, Yang FD, Wang G, Fang YR, Lu Z, Yang HC, Hu J, Chen ZY, Huang Y, Sun J, Wang XP, Li HC, Zhang JB, Osser DN, Si TM. Prevalence and clinical features of atypical depression among patients with major depressive disorder in China. J Affect Disord 2019; 246:285-289. [PMID: 30594041 DOI: 10.1016/j.jad.2018.12.020] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 12/06/2018] [Accepted: 12/15/2018] [Indexed: 02/05/2023]
Abstract
BACKGROUND Little is known about the demographic and clinical features of the atypical subtype of major depressive disorder (MDD) patients in China. This study set out to investigate the prevalence of atypical depression in MDD patients in China, and identify its demographic and clinical features. METHODS The study was conducted in 13 major psychiatric hospitals or in the psychiatric units of general hospitals in China, and recruited a sample of 1172 patients diagnosed with MDD. The patients' demographic and clinical features and prescriptions of psychotropic drugs were collected using a standardized questionnaire designed for the study. RESULTS The prevalence of atypical depression was 15.3%. In multiple logistic regression analyses, compared to the non-atypical depression patients, the atypical depression patients were more likely to have depressive episodes with suicide ideation and attempts (OR = 1.49, 95% CI = 1.06, 2.10, P = 0.023), depressive episodes with psychotic features (OR = 2.15, 95% CI = 1.43, 3.22, P < 0.001), seasonal depressive episodes (OR = 1.77, 95% CI = 1.12, 2.78, P = 0.014), an earlier age of onset (OR = 0.98, 95% CI = 0.96, 0.99, P = 0.001), and lifetime depressive episodes (OR = 1.07, 95% CI = 1.01, 1.13, P = 0.020). LIMITATIONS The assessment of atypical features was not based on a validated rating scale. CONCLUSION Our results indicate that atypical depression is common in Chinese patients with MDD. MDD with atypical features may be more severe and debilitating than patients with non-atypical features.
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Affiliation(s)
- Li-Min Xin
- Key Laboratory of Mental Health, Ministry of Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital and Peking University Institute of Mental Health, Beijing 100191, China; Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China
| | - Lin Chen
- Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China
| | - Yun-Ai Su
- Key Laboratory of Mental Health, Ministry of Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital and Peking University Institute of Mental Health, Beijing 100191, China.
| | - Fu-De Yang
- Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China
| | - Gang Wang
- Mood Disorders Center, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Yi-Ru Fang
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zheng Lu
- Shanghai Tongji Hospital, Tongji University Medical School, Shanghai, China
| | - Hai-Chen Yang
- Division of Mood Disorders, Shenzhen Mental Health Centre, Shenzhen, Guangdong Province, China
| | - Jian Hu
- The First Hospital of Harbin Medical University, Harbin, China
| | - Zhi-Yu Chen
- Hangzhou Seventh People's Hospital, Hangzhou, China
| | - Yi Huang
- West China Hospital, Sichuan University, Chengdu, China
| | - Jing Sun
- The Affiliated Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Xiao-Ping Wang
- Mental Health Institute, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Hui-Chun Li
- The Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jin-Bei Zhang
- The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - David N Osser
- Harvard Medical School Department of Psychiatry and VA Boston Healthcare System, Boston, MA, United States
| | - Tian-Mei Si
- Key Laboratory of Mental Health, Ministry of Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital and Peking University Institute of Mental Health, Beijing 100191, China.
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8
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Musil R, Seemüller F, Meyer S, Spellmann I, Adli M, Bauer M, Kronmüller KT, Brieger P, Laux G, Bender W, Heuser I, Fisher R, Gaebel W, Schennach R, Möller HJ, Riedel M. Subtypes of depression and their overlap in a naturalistic inpatient sample of major depressive disorder. Int J Methods Psychiatr Res 2018; 27:e1569. [PMID: 29498147 PMCID: PMC6877097 DOI: 10.1002/mpr.1569] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2016] [Revised: 03/31/2017] [Accepted: 04/10/2017] [Indexed: 12/18/2022] Open
Abstract
Subtyping depression is important in order to further delineate biological causes of depressive syndromes. The aim of this study was to evaluate clinical and outcome characteristics of distinct subtypes of depression and to assess proportion and features of patients fulfilling criteria for more than one subtype. Melancholic, atypical and anxious subtypes of depression were assessed in a naturalistic sample of 833 inpatients using DSM-IV specifiers based on operationalized criteria. Baseline characteristics and outcome criteria at discharge were compared between distinct subtypes and their overlap. A substantial proportion of patients (16%) were classified with more than one subtype of depression, 28% were of the distinct anxious, 7% of the distinct atypical and 5% of the distinct melancholic subtype. Distinct melancholic patients had shortest duration of episode, highest baseline depression severity, but were more often early improvers; distinct anxious patients had higher NEO-Five Factor Inventory (NEO-FFI) neuroticism scores compared with patients with unspecific subtype. Melancholic patients with overlap of anxious features had worse treatment outcome compared to distinct melancholic and distinct anxious subtype. Distinct subtypes differed in only few variables and patients with overlap of depression subtypes may have independent clinical and outcome characteristics. Studies investigating biological causes of subtypes of depression should take influence of features of other subtypes into account.
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Affiliation(s)
- Richard Musil
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Florian Seemüller
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany.,Department of Psychiatry and Psychotherapy, kbo-Lech-Mangfall-Klinik, Garmisch-Partenkirchen, Garmisch-Partenkirchen, Germany
| | - Sebastian Meyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany.,Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zürich, Switzerland.,Institute of Medical Informatics, Biometry, and Epidemiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Ilja Spellmann
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany.,Bezirkskrankenhaus Kaufbeuren, Bezirkskliniken Schwaben, Kaufbeuren, Germany
| | - Mazda Adli
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte (CCM), Berlin, Germany
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Klaus-Thomas Kronmüller
- Department of Psychiatry and Psychotherapy, University of Heidelberg, Heidelberg, Germany.,LWL-Klinikum, Gütersloh, Germany
| | - Peter Brieger
- Department of Psychiatry and Psychotherapy, Martin-Luther University Halle-Wittenberg, Halle, Germany.,Department of Psychiatry and Psychotherapy, kbo-Isar-Amper-Klinikum Munich East, Haar, Gemany
| | - Gerd Laux
- kbo-Inn-Salzach-Klinikum, Department of Psychiatry and Psychotherapy, Wasserburg, Gemany
| | - Wolfram Bender
- Department of Psychiatry and Psychotherapy, kbo-Isar-Amper-Klinikum Munich East, Haar, Gemany
| | - Isabella Heuser
- Department of Psychiatry, Charité - Campus Benjamin Franklin (CBF), Berlin, Germany
| | - Robert Fisher
- Department of Psychiatry and Psychotherapy, Auguste-Viktoria-Krankenhaus, Berlin, Germany.,South Hackney CMHT, Donald WinniCott Centre, London, UK
| | - Wolfgang Gaebel
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany
| | - Rebecca Schennach
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany.,Schön Klinik Roseneck, Prien, Rosenheim, Prien am Chiemsee, Germany
| | - Hans-Jürgen Möller
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Michael Riedel
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany.,Klinik für Psychiatrie & Psychotherapie II, Zentrum für Psychiatrie Calw Klinikum Nordschwarzwald, Calw-Hirsau, Germany
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Özdemir O, Kurdoglu Z, Yıldız S, Özdemir PG, Yilmaz E. The relationship between atypical depression and insülin resistance in patients with polycystic ovary syndrome and major depression. Psychiatry Res 2017; 258:171-176. [PMID: 28168992 DOI: 10.1016/j.psychres.2016.11.043] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 08/19/2016] [Accepted: 11/27/2016] [Indexed: 10/20/2022]
Abstract
In this study, we aimed to examine the relationship between atypical depression and insulin resistance (IR) in patients with polycystic ovary syndrome (PCOS) and major depression. A total of 176 subjects (69 patients with PCOS, 58 patients with depression, and 49 healthy controls) were included in the study. The Beck Depression Inventory (BDI), the Beck Anxiety Inventory (BAI), the Beck Hopelessness Scale (BHS), and the Scale for Suicide Ideation (SSI) were administered. Data concerning their height, weight, fasting a.m. serum levels of insulin, glucose level, and total testosterone level were collected from all participants. The body mass index (BMI) and the Homeostasis Model Assessment Insulin Resistance index (HOMA-IR) were both calculated. 34 (49.3%) of the PCOS patients met the criteria for depression. 26 (76.5%) of them had atypical depression, 8 (23.5%) had non-atypical depression. 27 (46.6%) of the 58 depressed patients had atypical depression. Insulin resistance was higher in the PCOS patients than in the control subjects and the depression patients. There was no association between atypical depression and IR in patients with PCOS and depression. We concluded that there is no relationship between IR and atypical depression.
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Affiliation(s)
- Osman Özdemir
- Yuzuncu Yil University, Faculty of Medicine, Department of Psychiatry, Van, Turkey.
| | - Zehra Kurdoglu
- Department of Obstetrics and Gynecology, Ankara Training and Research Hospital, Ankara, Turkey.
| | - Saliha Yıldız
- Yuzuncu Yil University, Faculty of Medicine, Department of Endocrinology, Van, Turkey.
| | - Pınar Güzel Özdemir
- Yuzuncu Yil University, Faculty of Medicine, Department of Psychiatry, Van, Turkey.
| | - Ekrem Yilmaz
- Yuzuncu Yil University, Faculty of Medicine, Department of Psychiatry, Van, Turkey.
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Simões do Couto F, Lunet N, Ginó S, Chester C, Freitas V, Maruta C, Figueira ML, de Mendonça A. Depression with melancholic features is associated with higher long-term risk for dementia. J Affect Disord 2016; 202:220-9. [PMID: 27267294 DOI: 10.1016/j.jad.2016.05.026] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Revised: 04/30/2016] [Accepted: 05/17/2016] [Indexed: 11/26/2022]
Abstract
BACKGROUND Depression has been reported to increase the risk of subsequently developing dementia, but the nature of this relation remains to be elucidated. Depression can be a prodrome/manifestation of dementia or an early risk factor, and the effect may differ according to depression subtypes. Our aim was to study the association between early-onset depression and different depression subtypes, and the later occurrence of dementia. METHODS We conducted a cohort study including 322 subjects with depression, recruited between 1977 and 1984. A comparison cohort (non-exposed) was recruited retrospectively, to include 322 subjects admitted at the same hospital for routine surgery (appendicectomy or cholecystectomy), at the same period as the depressed cohort. Subjects were contacted again between 2009 and 2014, to assess their dementia status. We computed the risk for dementia in subjects with early onset depression and quantified the association between different depression subtypes (namely melancholic, anxious, and psychotic) and dementia. RESULTS The odds of dementia were increased by 2.90 times (95% C.I. 1.61-5.21; p<0.0001) for the depressed cohort when compared to the surgical cohort. When the analysis was restricted to patients younger than 45 years old at baseline, the odds for dementia in the depressed cohort were also significantly higher when compared to the surgical cohort (8.53; 95% C.I. 2.40-30.16). In the multivariate Cox analysis, subjects having depression with melancholic features had an increased risk for developing dementia compared to those without melancholic features (HR=3.64; 95% C.I. 1.78-11.26; p=0.025). LIMITATIONS About 59% of the participants with depression and 53% of those non-exposed were lost during follow up. The inclusion of biological biomarkers would strengthen the results. The sample included a low number of bipolar patients. CONCLUSIONS These results support depression as an early risk factor for dementia. Depression with melancholic features was found as an important risk factor for dementia, playing a main role in the relation between these disorders.
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Affiliation(s)
- Frederico Simões do Couto
- Dementia Study Group, Institute of Molecular Medicine, Faculdade de Medicina, Universidade de Lisboa, Portugal; Psychiatry and Psychology Department, Faculdade de Medicina, Universidade de Lisboa, Portugal.
| | - Nuno Lunet
- Department of Clinical Epidemiology, Predictive Medicine and Public Health, Faculty of Medicine, University of Porto, Porto, Portugal; EPIUnit - Institute of Public Health, University of Porto, Porto, Portugal
| | - Sandra Ginó
- Dementia Study Group, Institute of Molecular Medicine, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | - Catarina Chester
- Dementia Study Group, Institute of Molecular Medicine, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | - Vanda Freitas
- Dementia Study Group, Institute of Molecular Medicine, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | - Carolina Maruta
- Dementia Study Group, Institute of Molecular Medicine, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | - Maria Luísa Figueira
- Psychiatry and Psychology Department, Faculdade de Medicina, Universidade de Lisboa, Portugal
| | - Alexandre de Mendonça
- Dementia Study Group, Institute of Molecular Medicine, Faculdade de Medicina, Universidade de Lisboa, Portugal
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11
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Evidence for Broadening Criteria for Atypical Depression Which May Define a Reactive Depressive Disorder. PSYCHIATRY JOURNAL 2015; 2015:575931. [PMID: 26258131 PMCID: PMC4516843 DOI: 10.1155/2015/575931] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/03/2015] [Accepted: 07/05/2015] [Indexed: 11/17/2022]
Abstract
Objective. Arguing that additional symptoms should be added to the criteria for atypical depression. Method. Published research articles on atypical depression are reviewed. Results. (1) The original studies upon which the criteria for atypical depression were based cited fatigue, insomnia, pain, and loss of weight as characteristic symptoms. (2) Several studies of DSM depressive criteria found patients with atypical depression to exhibit high levels of insomnia, fatigue, and loss of appetite/weight. (3) Several studies have found atypical depression to be comorbid with headaches, bulimia, and body image issues. (4) Most probands who report atypical depression meet criteria for "somatic depression," defined as depression associated with several of disordered eating, poor body image, headaches, fatigue, and insomnia. The gender difference in prevalence of atypical depression results from its overlap with somatic depression. Somatic depression is associated with psychosocial measures related to gender, linking it with the descriptions of atypical depression as "reactive" appearing in the studies upon which the original criteria for atypical depression were based. Conclusion. Insomnia, disordered eating, poor body image, and aches/pains should be added as criteria for atypical depression matching criteria for somatic depression defining a reactive depressive disorder possibly distinct from endogenous melancholic depression.
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12
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Lutz M, Morali A, Lang JP. La dépression atypique : perspectives cliniques. Encephale 2013; 39:258-64. [DOI: 10.1016/j.encep.2012.08.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2011] [Accepted: 08/08/2012] [Indexed: 10/27/2022]
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13
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Dere J, Sun J, Zhao Y, Persson TJ, Zhu X, Yao S, Bagby RM, Ryder AG. Beyond "somatization" and "psychologization": symptom-level variation in depressed Han Chinese and Euro-Canadian outpatients. Front Psychol 2013; 4:377. [PMID: 23818884 PMCID: PMC3694214 DOI: 10.3389/fpsyg.2013.00377] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2012] [Accepted: 06/07/2013] [Indexed: 11/13/2022] Open
Abstract
The finding that people of Chinese heritage tend to emphasize somatic rather than psychological symptoms of depression has frequently been discussed in the culture and mental health literature since the 1970s. Recent studies have confirmed that Chinese samples report more somatic and fewer psychological depression symptoms compared to "Western" samples. The question remains, however, as to whether or not these effects are attributable to variation in all the constituent symptoms or to a subset. If the latter, there is the additional possibility that some symptoms might show a divergent pattern. Such findings would have implications for how cultural variations in symptom presentation are interpreted, and would also inform the cultural study of affective experiences more broadly. The current study addressed these issues in Chinese (n = 175) and Euro-Canadian (n = 107) psychiatric outpatients originally described by Ryder et al. (2008). Differential item functioning (DIF) was used to examine whether specific somatic and psychological symptoms diverged from the overall patterns of cultural variation. Chi-square analyses were used to examine atypical somatic symptoms (e.g., hypersomnia), previously neglected in this literature. No DIF was observed for the typical somatic symptoms, but Euro-Canadians reported greater levels of atypical somatic symptoms, and showed higher rates of atypical depression. DIF was observed for psychological symptoms-the Chinese reported high levels of "suppressed emotions" and "depressed mood," relative to their overall psychological symptom reporting. Chinese outpatients also spontaneously reported "depressed mood" at similar levels as the Euro-Canadians, contrary to prevailing ideas about Chinese unwillingness to discuss depression. Overall, the findings provide a more nuanced picture of how culture shapes symptom presentation and point toward future studies designed to unpack cultural variation in narrower subsets of depressive symptoms.
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Affiliation(s)
- Jessica Dere
- Social Aetiology of Mental Illness CIHR Training Program, Centre for Addiction and Mental Health, University of TorontoToronto, ON, Canada
| | - Jiahong Sun
- Department of Psychology, Concordia UniversityMontreal, QC, Canada
| | - Yue Zhao
- Department of Psychology, Concordia UniversityMontreal, QC, Canada
| | - Tonje J. Persson
- Department of Psychology, Concordia UniversityMontreal, QC, Canada
| | - Xiongzhao Zhu
- Medical Psychological Institute of the Second Xiangya Hospital, Central South UniversityChangsha, China
| | - Shuqiao Yao
- Medical Psychological Institute of the Second Xiangya Hospital, Central South UniversityChangsha, China
| | - R. Michael Bagby
- Departments of Psychology and Psychiatry, University of TorontoToronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental HealthToronto, ON, Canada
| | - Andrew G. Ryder
- Department of Psychology, Concordia UniversityMontreal, QC, Canada
- Lady Davis Institute and the Culture and Mental Health Research Unit, SMBD–Jewish General HospitalMontreal, QC, Canada
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14
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Seemüller F, Segmiller F, Obermeier M, Möller HJ. [Sleep disorders in major psychiatric diseases]. MMW Fortschr Med 2012; 154:53-56. [PMID: 22880300 DOI: 10.1007/s15006-012-0862-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
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15
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Gan Z, Diao F, Wei Q, Wu X, Cheng M, Guan N, Zhang M, Zhang J. A predictive model for diagnosing bipolar disorder based on the clinical characteristics of major depressive episodes in Chinese population. J Affect Disord 2011; 134:119-25. [PMID: 21684010 DOI: 10.1016/j.jad.2011.05.054] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2011] [Revised: 05/24/2011] [Accepted: 05/27/2011] [Indexed: 01/23/2023]
Abstract
BACKGROUND A correct timely diagnosis of bipolar depression remains a big challenge for clinicians. This study aimed to develop a clinical characteristic based model to predict the diagnosis of bipolar disorder among patients with current major depressive episodes. METHODS A prospective study was carried out on 344 patients with current major depressive episodes, with 268 completing 1-year follow-up. Data were collected through structured interviews. Univariate binary logistic regression was conducted to select potential predictive variables among 19 initial variables, and then multivariate binary logistic regression was performed to analyze the combination of risk factors and build a predictive model. Receiver operating characteristic (ROC) curve was plotted. RESULTS Of 19 initial variables, 13 variables were preliminarily selected, and then forward stepwise exercise produced a final model consisting of 6 variables: age at first onset, maximum duration of depressive episodes, somatalgia, hypersomnia, diurnal variation of mood, irritability. The correct prediction rate of this model was 78% (95%CI: 75%-86%) and the area under the ROC curve was 0.85 (95%CI: 0.80-0.90). The cut-off point for age at first onset was 28.5 years old, while the cut-off point for maximum duration of depressive episode was 7.5 months. LIMITATIONS The limitations of this study include small sample size, relatively short follow-up period and lack of treatment information. CONCLUSION Our predictive models based on six clinical characteristics of major depressive episodes prove to be robust and can help differentiate bipolar depression from unipolar depression.
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Affiliation(s)
- Zhaoyu Gan
- Department of Psychiatry, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
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16
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Bürgy M. [On the differential diagnostics of depersonalization experiences]. DER NERVENARZT 2011; 83:40-4, 46-8. [PMID: 21301801 DOI: 10.1007/s00115-011-3248-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Depersonalization represents an unspecific symptom which is to be found across the entire spectrum of psychiatric nosology. Delineating the historical lines of development of the depersonalization concept and reviewing existing psychopathological experiential knowledge reveals that depersonalization is underpinned by highly diverse modes of experience. In terms of differential diagnostics at the symptom level, a distinction can be made between depersonalization as a neurotic phenomenon on the one hand and a psychotic form occurring in schizophrenia and melancholia on the other. The reference points defined here extend beyond current descriptive classifications and open up the diagnostic process to allow an inclusion of etiological and therapeutic aspects.
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Affiliation(s)
- M Bürgy
- Klinik für Spezielle Psychiatrie, Sozialpsychiatrie und Psychotherapie, Zentrum für Seelische Gesundheit, Klinikum Stuttgart.
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17
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Michal M, Wiltink J, Till Y, Wild PS, Münzel T, Blankenberg S, Beutel ME. Type-D personality and depersonalization are associated with suicidal ideation in the German general population aged 35-74: results from the Gutenberg Heart Study. J Affect Disord 2010; 125:227-33. [PMID: 20206385 DOI: 10.1016/j.jad.2010.02.108] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2009] [Revised: 02/05/2010] [Accepted: 02/05/2010] [Indexed: 10/19/2022]
Abstract
BACKGROUND Suicidal ideation (SID) is a major risk factor for suicide attempts. Mental disorders are among the strongest correlates of suicide, with depression and anxiety disorders playing a major role. The present study aims to investigate the contribution of under researched factors contributing to SID such as depersonalization, Type-D personality and cardiovascular risk factors. METHODS Factors associated with SID were investigated in a sample of N=5000 participants (aged 35-74 years) of the community-based survey "Gutenberg Heart Study". The factors were assessed by self-report instruments, computer-assisted interviews and medical examination. RESULTS 7.5% of the sample reported SID over the last 2 weeks. In the univariate analysis SID was significantly associated with female sex, living without a partner, low socioeconomic status, diagnosis of coronary heart disease, family history of myocardial infarction, smoking and mental distress. In the full adjusted model significant associations remained with age (in years) OR 1.02 (95%CI 1.01-1.04, p=0.002), self-reported depression OR 3.21 (95%CI 2.23-4.62, p<0.0001), panic disorder OR 1.56 (95%CI 1.03-2.36, p=0.036), depersonalization OR 2.45 (95%CI 1.78-3.38, p<0.0001), Type-D personality OR 1.98 (95%CI 1.49-2.63, p<0.0001) and impairment by mental distress OR 2.15 (95%CI 1.74-2.67, p<0.0001). LIMITATIONS Main limitations are the reliance on self-report measures of SID and of mental distress. CONCLUSIONS For the first time it has been shown that in the general population depersonalization and Type-D personality are uniquely associated with SID. These associations need further elucidation.
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Affiliation(s)
- Matthias Michal
- Department of Psychosomatic Medicine and Psychotherapy, University Medical Center of the Johannes Gutenberg University Mainz, Germany.
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19
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Duan DM, Tu Y, Chen LP, Wu ZJ. Efficacy evaluation for depression with somatic symptoms treated by electroacupuncture combined with Fluoxetine. J TRADIT CHIN MED 2010; 29:167-73. [PMID: 19894377 DOI: 10.1016/s0254-6272(09)60057-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
OBJECTIVE This study is to investigate the clinical therapeutic effects and safety of treating mild or moderate depression with somatic symptoms with electroacupuncture combined with Fluoxetine. METHODS 95 cases of mild or moderate depression with somatic symptoms were randomly divided into a Fluoxetine group, and an electroacupuncture plus Fluoxetine group. Hamilton Depression Scale (HAMD) was used for the assessment of clinical therapeutic effects and Treatment Emergent Symptom Scale (TESS) was used for assessment of adverse reactions. RESULTS The total effective rate was 77.27% in the Fluoxetine group and 78.26% in the electroacupuncture plus Fluoxetine group, showing no statistically significant difference between these two groups (P > 0.05). However, the treatment took effect after two weeks in the electroacupuncture plus Fluoxetine group but after four weeks in Fluoxetine group. During this time, a better therapeutic effect on depression with mild or moderate somatic symptoms was found in the electroacupuncture plus Fluoxetine group, which also had fewer adverse reactions than the Fluoxetine group. CONCLUSION Electroacupuncture combined with Fluoxetine takes effect faster for relieving the somatic symptoms with fewer adverse reactions. It is worth popularizing clinically.
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Affiliation(s)
- Dong-Mei Duan
- Department of TCM and Acupuncture, Chinese PLA General Hospital, Beijing 100853, China
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20
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Thuile J, Even C, Musa C, Friedman S, Rouillon F. Clinical correlates of atypical depression and validation of the French version of the Scale for Atypical Symptoms (SAS). J Affect Disord 2009; 118:113-7. [PMID: 19272652 DOI: 10.1016/j.jad.2009.02.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2008] [Revised: 02/06/2009] [Accepted: 02/06/2009] [Indexed: 11/15/2022]
Abstract
BACKGROUND The 8-item "Scale for Atypical Symptoms" (SAS) and its structured interview, the SIGH-SAD, have been developed to assess atypical symptoms of depression in winter depression. Although they are commonly used, no validation study has yet been conducted. METHODS 270 consecutive depressed inpatients were assessed prospectively. Pearson's correlation coefficients between fulfilment of Liebowitz criteria for atypical depression and both the SAS score and the atypical balance [ratio of the AS score to the total score on the Hamilton Depression Rating Scale 29-item (HDRS-29)] were calculated. The SAS was evaluated against Liebowitz criteria using binary logistic regression. A ROC curve was performed with the atypical balance against the fulfilment of Liebowitz criteria. RESULTS 18.5% of patients met the criteria for atypical depression. The presence of an atypical depression was significantly correlated with both the atypical score (r=0.42) and the atypical balance (r=0.51). The logistic regression showed that a higher score on the SAS, the absence of a somatic syndrome (ICD-10) and a lower HDRS-21 score were independent predictors of an atypical depression while age, gender and bipolarity were not. The ROC curve showed that an atypical balance of 29% was the optimal threshold for the diagnosis of atypical depression (sensitivity=0.86, specificity=0.79). LIMITATION Patients with bipolar I and II were not distinguished. CONCLUSION Atypical depression is relatively frequent in hospitalised patients. The concurrent validity of the French version of the SAS and its structured interview, the SIGH-SAD is satisfactory.
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Affiliation(s)
- J Thuile
- Clinique des Maladies Mentales et de l'Encéphale, Centre Hospitalier Sainte-Anne, Université Paris V René Descartes, Paris, France.
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21
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Kang EH, Lee IS, Chung SK, Lee SY, Kim EJ, Hong JP, Oh KS, Woo JM, Kim S, Park JE, Yu BH. Mirtazapine versus venlafaxine for the treatment of somatic symptoms associated with major depressive disorder: a randomized, open-labeled trial. Psychiatry Res 2009; 169:118-23. [PMID: 19695711 DOI: 10.1016/j.psychres.2008.06.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2007] [Revised: 05/27/2008] [Accepted: 06/12/2008] [Indexed: 11/19/2022]
Abstract
Somatic symptoms are often important in the treatment of major depressive disorder (MDD). The aim of this open-labeled trial was to examine the efficacy of mirtazapine for the treatment of MDD with clinically significant somatic symptoms, as compared with venlafaxine. A total of 126 patients with MDD (score >/=18 on the Hamilton Rating Scale for Depression-17) were included in both the intent-to-treat (n=73 in the mirtazapine group and n=53 in the venlafaxine group) and completer analysis (n=51 and n=37, respectively). After treatment, both treatment groups showed similar improvements in depressive symptoms. Repeated measures analysis of variance for the intent-to-treat population revealed that there were no significant differences in mean change of the Symptom Check List-90-Revised (SCL-90-R) somatization subscores between the two groups. For completers, there was a significant timextreatment interaction in the SCL-90-R somatization subscores, but the differences between the two groups at endpoint did not reach statistical significance in post-hoc analysis. In conclusion, this study suggests that overall efficacies of mirtazapine and venlafaxine are similar for the treatment of overall symptoms in MDD, and both drugs may be useful for the treatment of somatic symptoms in MDD patients.
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Affiliation(s)
- Eun-Ho Kang
- Department of Psychiatry, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
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Nandi A, Beard JR, Galea S. Epidemiologic heterogeneity of common mood and anxiety disorders over the lifecourse in the general population: a systematic review. BMC Psychiatry 2009; 9:31. [PMID: 19486530 PMCID: PMC2700109 DOI: 10.1186/1471-244x-9-31] [Citation(s) in RCA: 106] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2008] [Accepted: 06/01/2009] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Clinical evidence has long suggested there may be heterogeneity in the patterns and predictors of common mood and anxiety disorders; however, epidemiologic studies have generally treated these outcomes as homogenous entities. The objective of this study was to systematically review the epidemiologic evidence for potential patterns of heterogeneity of common mood and anxiety disorders over the lifecourse in the general population. METHODS We reviewed epidemiologic studies examining heterogeneity in either the nature of symptoms experienced ("symptom syndromes") or in patterns of symptoms over time ("symptom trajectories"). To be included, studies of syndromes were required to identify distinct symptom subtypes, and studies of trajectories were required to identify distinct longitudinal patterns of symptoms in at least three waves of follow-up. Studies based on clinical or patient populations were excluded. RESULTS While research in this field is in its infancy, we found growing evidence that, not only can mood and anxiety disorders be differentiated by symptom syndromes and trajectories, but that the factors associated with these disorders may vary between these subtypes. Whether this reflects a causal pathway, where genetic or environmental factors influence the nature of the symptom or trajectory subtype experienced by an individual, or whether individuals with different subtypes differed in their susceptibility to different environmental factors, could not be determined. Few studies addressed issues of comorbidity or transitions in symptoms between common disorders. CONCLUSION Understanding the diversity of these conditions may help us identify preventable factors that are only associated with some subtypes of these common disorders.
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Affiliation(s)
- Arijit Nandi
- Center for Population and Development Studies, Harvard School of Public Health, Boston, USA
| | - John R Beard
- Center for Urban Epidemiologic Studies, New York Academy of Medicine, New York, USA
- School of Public Health, University of Sydney, Sydney, Australia
- Faculty of Health and Applied Science, Southern Cross University, Lismore, Australia
| | - Sandro Galea
- Center for Urban Epidemiologic Studies, New York Academy of Medicine, New York, USA
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, USA
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, USA
- Survey Research Center, Institute for Social Research, Ann Arbor, USA
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