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Liu S, Ren H, Li Y, Liu Y, Fu S, Han ZR. Gender Difference in the Onset of Adolescent Depressive Symptoms: A Cross-Lagged Panel Network Analysis. Res Child Adolesc Psychopathol 2024:10.1007/s10802-024-01235-4. [PMID: 39215790 DOI: 10.1007/s10802-024-01235-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/29/2024] [Indexed: 09/04/2024]
Abstract
Depressive symptoms are prevalent in adolescents, especially girls, underscoring the need for early detection and targeted interventions. Identifying initial symptoms and their temporal associations is vital for such interventions. This study used cross-lagged panel network (CLPN) analysis to examine the central depressive symptoms and their interconnections within a national cohort derived from the China Family Panel Study (CFPS). The participants included 2524 adolescents (45.8% girls), with depressive symptoms assessed using the Epidemiological Studies Depression Scale (CES-D-8) in 2016 (Mage = 12.30) and 2018 (Mage = 14.25). The CLPN model showed that "loneliness" and "not getting going (fatigue)" at T1 were the strongest predictors of subsequent depressive symptoms at T2, after controlling for demographic variables and depressive symptoms at T1. Conversely, depressed mood and anhedonia at T2 were most likely to be influenced by other symptoms at T1. Gender-stratified analyses identified "loneliness" as the initial symptom in girls and "fatigue" for boys. Additionally, girls exhibited stronger reciprocal associations among depressive symptoms than boys. The findings suggest that addressing interpersonal loneliness is crucial for adolescent girls, whereas somatic fatigue should be a focus for adolescent boys, highlighting the need for gender-specific approaches in early intervention strategies. This research provides insights into the distinct gendered networks of depressive symptomatology in adolescents, informing tailored prevention and intervention efforts.
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Affiliation(s)
- Sihan Liu
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Faculty of Psychology, Beijing Normal University, No.19 Xinjiekouwai St, Haidian District, Beijing, China
| | - Haining Ren
- T. Denny Sanford School of Social and Family Dynamics, Arizona State University, Tempe, USA
| | - Yijia Li
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Faculty of Psychology, Beijing Normal University, No.19 Xinjiekouwai St, Haidian District, Beijing, China
| | - Yang Liu
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Faculty of Psychology, Beijing Normal University, No.19 Xinjiekouwai St, Haidian District, Beijing, China
| | - Sinan Fu
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Faculty of Psychology, Beijing Normal University, No.19 Xinjiekouwai St, Haidian District, Beijing, China
| | - Zhuo Rachel Han
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Faculty of Psychology, Beijing Normal University, No.19 Xinjiekouwai St, Haidian District, Beijing, China.
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2
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Zhao D, Gao X, Chen W, Zhou Q. How Coparenting Is Linked to Depression among Chinese Young Girls and Boys: Evidence from a Network Analysis. Behav Sci (Basel) 2024; 14:297. [PMID: 38667093 PMCID: PMC11047583 DOI: 10.3390/bs14040297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/15/2024] [Accepted: 03/29/2024] [Indexed: 04/29/2024] Open
Abstract
This study aimed to explore the relationship between parental coparenting and depression among Chinese young adolescents and potential gender differences via network analysis. Thus, 793 fourth-grade students (girls: 281 (35.40%), Mage = 9.99 years, SD = 0.59 years) were recruited from three primary schools in Northern China. The young adolescents rated their depression and perceived paternal and maternal coparenting. Network analysis was used to detect the central nodes and bridge mechanisms among coparenting and depressive components. The results indicated that paternal and maternal consistency as well as maternal conflict were the most central components in the coparenting-depression network. Paternal consistency, maternal conflict and paternal disparagement in coparenting, as well as somatic complaints and positive affect in adolescents' depression, exhibited high bridge strengths, suggesting those constructs served as vital bridges to connect the two subnetworks. Moreover, paternal consistency showed a higher bridge strength in the boys' network than the girls' one, whereas the edge linking adolescents' positive affect to paternal disparagement and integrity was stronger in the girls' network. This study contributes to the understanding of associations between parental coparenting and young adolescents' depression and offered insights into targeted interventions for early adolescent depression by enhancing parental coparenting.
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Affiliation(s)
- Demao Zhao
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing 100875, China; (D.Z.); (X.G.)
| | - Xin Gao
- Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing 100875, China; (D.Z.); (X.G.)
| | - Wei Chen
- School of Education, Tianjin University, Tianjin 300350, China;
| | - Quan Zhou
- Institute of Developmental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China
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3
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Rosenström TH, Czajkowski NO, Solbakken OA, Saarni SE. Direction of dependence analysis for pre-post assessments using non-Gaussian methods: a tutorial. Psychother Res 2023; 33:1058-1075. [PMID: 36706267 DOI: 10.1080/10503307.2023.2167526] [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: 06/23/2022] [Accepted: 12/28/2022] [Indexed: 01/28/2023] Open
Abstract
OBJECTIVE We introduced methods for solving causal direction of dependence between variables observed in pre- and post-psychotherapy assessments, showing how to apply them and investigate their properties via simulations. In addition, we investigated whether changes in depressive symptoms drive changes in social and occupational functioning as suggested by the phase model of psychotherapy or vice versa, or neither. METHOD As a Gaussian (normal-distribution) model is unidentifiable here, we used an identifiable linear non-Gaussian structural vector autoregression model, conceptualizing instantaneous effects as during-psychotherapy causation and lagged effects as pre-treatment predictors of change. We tested six alternative estimators in six simulation settings that captured different real-world scenarios, and used real psychotherapy data from 1428 adult patients (Finnish Psychotherapy Quality Registry; assessments on Patient Health Questionnaire-9 and Social and Occupational Functioning Assessment Schedule). RESULTS The methodology was successful in identifying causal directions in simulated data. The real-data results provided no evidence for single direction of dependence, suggesting shared or reciprocal causation. CONCLUSIONS A powerful new tool was presented to investigate the process of psychotherapy using observational data. Application to patient data suggested that depression symptoms and functioning may reciprocate or reflect third variables instead of one predominantly driving the other during psychotherapy.
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Affiliation(s)
- Tom H Rosenström
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Nikolai O Czajkowski
- Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway
| | - Ole André Solbakken
- Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway
| | - Suoma E Saarni
- Brain Center, Department of Psychiatry, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
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4
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Malkki VK, Rosenström TH, Jokela MM, Saarni SE. Associations between specific depressive symptoms and psychosocial functioning in psychotherapy. J Affect Disord 2023; 328:29-38. [PMID: 36773764 DOI: 10.1016/j.jad.2023.02.021] [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: 04/28/2022] [Revised: 01/21/2023] [Accepted: 02/04/2023] [Indexed: 02/11/2023]
Abstract
BACKGROUND Psychotherapy for depression aims to reduce symptoms and to improve psychosocial functioning. We examined whether some symptoms are more important than others in the association between depression and functioning over the course of psychotherapy treatment. METHODS We studied associations between specific symptoms of depression (PHQ-9) and change in social and occupational functioning (SOFAS), both with structural equation models (considering liabilities of depression and each specific symptom) and with logistic regression models (considering the risk for individual patients). The study sample consisted of adult patients (n = 771) from the Finnish Psychotherapy Quality Registry (FPQR) who completed psychotherapy treatment between September 2018 and September 2021. RESULTS Based on our results of logistic regression analyses and SEM models, the baseline measures of depression symptoms were not associated with changes in functioning. Changes in depressed mood or hopelessness, problems with sleep, feeling tired, and feeling little interest or pleasure were associated with improved functioning during psychotherapy. The strongest evidence for symptom-specific effects was found for the symptom of depressed mood or hopelessness. LIMITATIONS Due to our naturalistic study design containing only two measurement points, we were unable to study the causal relationship between symptoms and functioning. CONCLUSIONS Changes in certain symptoms during psychotherapy may affect functioning independently of underlying depression. Knowledge about the dynamics between symptoms and functioning could be used in treatment planning or implementation. Depressed mood or hopelessness appears to have a role in the dynamic relationship between depression and functioning.
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Affiliation(s)
- Veera K Malkki
- Psychiatry, Helsinki University Hospital and University of Helsinki, Finland.
| | - Tom H Rosenström
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Finland
| | - Markus M Jokela
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Finland
| | - Suoma E Saarni
- Psychiatry, Helsinki University Hospital and University of Helsinki, Finland
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5
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Costello H, Roiser JP, Howard R. Antidepressant medications in dementia: evidence and potential mechanisms of treatment-resistance. Psychol Med 2023; 53:654-667. [PMID: 36621964 PMCID: PMC9976038 DOI: 10.1017/s003329172200397x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 10/13/2022] [Accepted: 12/20/2022] [Indexed: 01/10/2023]
Abstract
Depression in dementia is common, disabling and causes significant distress to patients and carers. Despite widespread use of antidepressants for depression in dementia, there is no evidence of therapeutic efficacy, and their use is potentially harmful in this patient group. Depression in dementia has poor outcomes and effective treatments are urgently needed. Understanding why antidepressants are ineffective in depression in dementia could provide insight into their mechanism of action and aid identification of new therapeutic targets. In this review we discuss why depression in dementia may be a distinct entity, current theories of how antidepressants work and how these mechanisms of action may be affected by disease processes in dementia. We also consider why clinicians continue to prescribe antidepressants in dementia, and novel approaches to understand and identify effective treatments for patients living with depression and dementia.
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Affiliation(s)
- Harry Costello
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Jonathan P. Roiser
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Robert Howard
- Division of Psychiatry, University College London, London, UK
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6
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Han S, Xu Y, Guo HR, Fang K, Wei Y, Liu L, Cheng J, Zhang Y, Cheng J. Two distinct subtypes of obsessive compulsive disorder revealed by a framework integrating multimodal neuroimaging information. Hum Brain Mapp 2022; 43:4254-4265. [PMID: 35726798 PMCID: PMC9435007 DOI: 10.1002/hbm.25951] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/14/2022] [Accepted: 05/06/2022] [Indexed: 12/03/2022] Open
Abstract
Patients with obsessive compulsive disorder (OCD) exhibit tremendous heterogeneity in structural and functional neuroimaging aberrance. However, most previous studies just focus on group‐level aberrance of a single modality ignoring heterogeneity and multimodal features. On that account, we aimed to uncover OCD subtypes integrating structural and functional neuroimaging features with the help of a multiview learning method and examined multimodal aberrance for each subtype. Ninety‐nine first‐episode untreated patients with OCD and 104 matched healthy controls (HCs) undergoing structural and functional MRI were included in this study. Voxel‐based morphometric and amplitude of low‐frequency fluctuation (ALFF) were adopted to assess gray matter volumes (GMVs) and the spontaneous neuronal fluctuations respectively. Structural/functional distance network was obtained by calculating Euclidean distance between pairs of regional GMVs/ALFF values across patients. Similarity network fusion, one of multiview learning methods capturing shared and complementary information from multimodal data sources, was used to fuse multimodal distance networks into one fused network. Then spectral clustering was adopted to categorize patients into subtypes. As a result, two robust subtypes were identified. These two subtypes presented opposite GMV aberrance and distinct ALFF aberrance compared with HCs while shared indistinguishable clinical and demographic features. In addition, these two subtypes exhibited opposite structure–function difference correlation reflecting distinct adaptive modifications between multimodal aberrance. Altogether, these results uncover two objective subtypes with distinct multimodal aberrance and provide a new insight into taxonomy of OCD.
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Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Yinhuan Xu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Hui-Rong Guo
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, China
| | - Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Liang Liu
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Junying Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, China.,Henan Engineering Research Center of Brain Function Development and Application, China
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7
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Belvederi Murri M, Grassi L, Caruso R, Nanni MG, Zerbinati L, Andreas S, Ausín B, Canuto A, Härter M, Lopez MM, Weber K, Wittchen HU, Volkert J, Alexopoulos GS. Depressive symptom complexes of community-dwelling older adults: a latent network model. Mol Psychiatry 2022; 27:1075-1082. [PMID: 34642459 DOI: 10.1038/s41380-021-01310-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/23/2021] [Accepted: 09/15/2021] [Indexed: 11/09/2022]
Abstract
Late-life depression has multiple, heterogeneous clinical presentations. The aim of the study was to identify higher-order homogeneous clinical features (symptom complexes), while accounting for their potential causal interactions within the network approach to psychopathology. We analyzed cross-sectional data from community-dwelling adults aged 65-85 years recruited by the European MentDis_ICF65+ study (n = 2623, mean age 74, 49% females). The severity of 33 depressive symptoms was derived from the age-adapted Composite International Diagnostic Interview. Symptom complexes were identified using multiple detection algorithms for symptom networks, and their fit to data was assessed with latent network models (LNMs) in exploratory and confirmatory analyses. Sensitivity analyses included the Partial Correlation Likelihood Test (PCLT) to investigate the data-generating structure. Depressive symptoms were organized by the Walktrap algorithm into eight symptom complexes, namely sadness/hopelessness, anhedonia/lack of energy, anxiety/irritability, self-reproach, disturbed sleep, agitation/increased appetite, concentration/decision making, and thoughts of death. An LNM adequately fit the distribution of individual symptoms' data in the population. The model suggested the presence of reciprocal interactions between the symptom complexes of sadness and anxiety, concentration and self-reproach and between self-reproach and thoughts of death. Results of the PCLT confirmed that symptom complex data were more likely generated by a network, rather than a latent-variable structure. In conclusion, late-life depressive symptoms are organized into eight interacting symptom complexes. Identification of the symptom complexes of late-life depression may streamline clinical assessment, provide targets for personalization of treatment, and aid the search for biomarkers and for predictors of outcomes of late-life depression.
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Affiliation(s)
- Martino Belvederi Murri
- Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy
| | - Luigi Grassi
- Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy
| | - Rosangela Caruso
- Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy
| | - Maria Giulia Nanni
- Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy
| | - Luigi Zerbinati
- Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy
| | - Sylke Andreas
- Department of Medical Psychology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.,Institute for Psychology, Universität Klagenfurt, A-9020, Klagenfurt, Austria
| | - Berta Ausín
- School of Psychology, Personality, Evaluation and Clinical Psychology Department, University Complutense of Madrid, Campus de Somosaguas s/n, 28223, Madrid, Spain
| | - Alessandra Canuto
- Division of Institutional Measures, University Hospitals of Geneva, 1208, Geneva, Switzerland
| | - Martin Härter
- Department of Medical Psychology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Manuel Muñoz Lopez
- School of Psychology, Personality, Evaluation and Clinical Psychology Department, University Complutense of Madrid, Campus de Somosaguas s/n, 28223, Madrid, Spain
| | - Kerstin Weber
- Division of Institutional Measures, University Hospitals of Geneva, 1208, Geneva, Switzerland
| | - Hans-Ulrich Wittchen
- Clinical Psychology & Psychotherapy RG, Department of Psychiatry & Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Jana Volkert
- Department of Psychosocial Prevention, University of Heidelberg, Bergheimer Str. 54, 69115, Heidelberg, Germany.,Institute of Psychology, University of Kassel, Holländische Str. 36-38, 34127, Kassel, Germany
| | - George S Alexopoulos
- Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine, White Plains, NY, USA.
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8
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Fernandez-Rodrigues V, Sanchez-Carro Y, Lagunas LN, Rico-Uribe LA, Pemau A, Diaz-Carracedo P, Diaz-Marsa M, Hervas G, de la Torre-Luque A. Risk factors for suicidal behaviour in late-life depression: A systematic review. World J Psychiatry 2022; 12:187-203. [PMID: 35111588 PMCID: PMC8783161 DOI: 10.5498/wjp.v12.i1.187] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 09/17/2021] [Accepted: 11/24/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Suicide is a leading cause of preventable death worldwide, with its peak of maximum incidence in later life. Depression often puts an individual at higher risk for suicidal behaviour. In turn, depression deserves particular interest in old age due to its high prevalence and dramatic impact on health and wellbeing. AIM To gather integrated evidence on the potential risk factors for suicide behaviour development in depressive older adults, and to examine the effects of depression treatment to tackle suicide behaviour in this population. METHODS A systematic review of empirical studies, published from 2000 onwards, was conducted. Suicidal behaviour was addressed considering its varying forms (i.e., wish to die, ideation, attempt, and completed suicide). RESULTS Thirty-five papers were selected for review, comprising both clinical and epidemiological studies. Most of studies focused on suicidal ideation (60%). The studies consistently pointed out that the risk was related to depressive episode severity, psychiatric comorbidity (anxiety or substance use disorders), poorer health status, and loss of functionality. Reduced social support and loneliness were also associated with suicide behaviour in depressive older adults. Finally, the intervention studies showed that suicidal behaviour was a robust predictor of depression treatment response. Reductions in suicidal ideation were moderated by reductions in risk factors for suicide symptoms. CONCLUSION To sum up, common and age-specific risk factors seem to be involved in suicide development in depressive older adults. A major effort should be made to tackle this serious public health concern so as to promote older people to age healthily and well.
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Affiliation(s)
| | - Yolanda Sanchez-Carro
- Department of Psychiatry, Universidad Autonoma de Madrid, Madrid 28046, Spain
- Centre for Biomedical Research in Mental Health (CIBERSAM), Madrid 28029, Spain
| | - Luisa Natalia Lagunas
- Department of Legal Medicine, Psychiatry and Pathology, Universidad Complutense de Madrid, Madrid 28046, Spain
| | - Laura Alejandra Rico-Uribe
- Centre for Biomedical Research in Mental Health (CIBERSAM), Madrid 28029, Spain
- Department of Psychology, La Rioja International University, Logrono 26006, Spain
| | - Andres Pemau
- Department of Psychology, Universidad Complutense de Madrid, Madrid 28223, Spain
| | | | - Marina Diaz-Marsa
- Centre for Biomedical Research in Mental Health (CIBERSAM), Madrid 28029, Spain
- Department of Legal Medicine, Psychiatry and Pathology, Universidad Complutense de Madrid, Madrid 28046, Spain
- Institute of Psychiatry and Mental Health, San Carlos Clinical Hospital, Madrid 28040, Spain
| | - Gonzalo Hervas
- Department of Psychology, Universidad Complutense de Madrid, Madrid 28223, Spain
| | - Alejandro de la Torre-Luque
- Centre for Biomedical Research in Mental Health (CIBERSAM), Madrid 28029, Spain
- Department of Legal Medicine, Psychiatry and Pathology, Universidad Complutense de Madrid, Madrid 28046, Spain
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9
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Evaluation of major depression symptom networks using clinician-rated and patient-rated data. J Affect Disord 2021; 292:583-591. [PMID: 34147971 DOI: 10.1016/j.jad.2021.05.102] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/18/2021] [Accepted: 05/30/2021] [Indexed: 11/20/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is heterogeneous, but official diagnostic classifications and widely used rating scales are based on the premise that MDD is a single disorder and that symptoms are equally important to assess severity. Also, patients and clinicians frequently diverge in how they evaluate MDD severity. In order to better understand the differences between MDD scales used by clinicians and patients in the context of MDD heterogeneity, we performed a network analysis from an approach that focuses on the interaction of symptoms rather than total score. METHODS The Hamilton Depression Rating Scale (HDRS) and the Beck Depression Inventory with 21 items (BDI) scored by the clinician or patient, respectively, were used to estimate the networks based on 794 MDD patients. The networks were estimated using software R 4.0.2 and Graphical Lasso, identifying communities of symptoms by the clique percolation method, and the mixed graphical models were used to evaluate the explained variance of each symptom. RESULTS The networks presented different communities of symptoms and connection structure (M = 0.177, p = 0.0028). The guilt connection strength and its association with suicidal ideation was greater in the BDI network. LIMITATIONS Transversal data from severe, chronic, or treatment resistant depression patients. CONCLUSIONS The present study suggests that the self-rated scale may perform better when assessing association between guilt and other symptoms, especially suicidal ideation. Communities of symptoms and edges between symptoms suggest that insomnia may be an independent symptom, thus requiring specific interventions. Some similar items are strongly connected and could be collapsed.
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10
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Wichers M, Riese H, Hodges TM, Snippe E, Bos FM. A Narrative Review of Network Studies in Depression: What Different Methodological Approaches Tell Us About Depression. Front Psychiatry 2021; 12:719490. [PMID: 34777038 PMCID: PMC8581034 DOI: 10.3389/fpsyt.2021.719490] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/30/2021] [Indexed: 11/13/2022] Open
Abstract
The network theory of psychopathology proposes that mental disorders arise from direct interactions between symptoms. This theory provides a promising framework to understand the development and maintenance of mental disorders such as depression. In this narrative review, we summarize the literature on network studies in the field of depression. Four methodological network approaches are distinguished: (i) studies focusing on symptoms at the macro-level vs. (ii) on momentary states at the micro-level, and (iii) studies based on cross-sectional vs. (iv) time-series (dynamic) data. Fifty-six studies were identified. We found that different methodological approaches to network theory yielded largely inconsistent findings on depression. Centrality is a notable exception: the majority of studies identified either positive affect or anhedonia as central nodes. To aid future research in this field, we outline a novel complementary network theory, the momentary affect dynamics (MAD) network theory, to understand the development of depression. Furthermore, we provide directions for future research and discuss if and how networks might be used in clinical practice. We conclude that more empirical network studies are needed to determine whether the network theory of psychopathology can indeed enhance our understanding of the underlying structure of depression and advance clinical treatment.
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Affiliation(s)
- Marieke Wichers
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation, Groningen, Netherlands
| | - Harriëtte Riese
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation, Groningen, Netherlands
| | - Taylor M Hodges
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation, Groningen, Netherlands
| | - Evelien Snippe
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation, Groningen, Netherlands
| | - Fionneke M Bos
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion regulation, Groningen, Netherlands.,University of Groningen, University Medical Center Groningen, Department of Psychiatry, Rob Giel Research Center, Groningen, Netherlands
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