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Depression: HELP
Articles by Albert M. van Hemert
Based on 39 articles published since 2008
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Between 2008 and 2019, A. van Hemert wrote the following 39 articles about Depression.
 
+ Citations + Abstracts
Pages: 1 · 2
1 Review Huntingtin gene repeat size variations affect risk of lifetime depression. 2017

Gardiner, Sarah L / van Belzen, Martine J / Boogaard, Merel W / van Roon-Mom, Willeke M C / Rozing, Maarten P / van Hemert, Albert M / Smit, Johannes H / Beekman, Aartjan T F / van Grootheest, Gerard / Schoevers, Robert A / Oude Voshaar, Richard C / Roos, Raymund A C / Comijs, Hannie C / Penninx, Brenda W J H / van der Mast, Roos C / Aziz, N Ahmad. ·Departments of Neurology, Leiden University Medical Centre, Leiden, The Netherlands. · Departments of Human Genetics, Leiden University Medical Centre, Leiden, The Netherlands. · Departments of Clinical Genetics, and Leiden University Medical Centre, Leiden, The Netherlands. · Department of Public Health, Section of Social Medicine, University of Copenhagen, Copenhagen, Denmark. · Departments of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands. · Department of Psychiatry, and EMGO Institute for Health and Care Research and Neuroscience Campus Amsterdam, VU University Medical Center/GGZ inGeest, Amsterdam, The Netherlands. · Department of Psychiatry, University Medical Centre Groningen, Groningen, The Netherlands. · Department of Psychiatry, Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Antwerp, Belgium. · Departments of Neurology, Leiden University Medical Centre, Leiden, The Netherlands. N.A.Aziz@lumc.nl. · Department of Neurodegenerative Disease, UCL Huntington's Disease Centre, University College London Institute of Neurology, London, United Kingdom. N.A.Aziz@lumc.nl. ·Transl Psychiatry · Pubmed #29225330.

ABSTRACT: Huntington disease (HD) is a severe neuropsychiatric disorder caused by a cytosine-adenine-guanine (CAG) repeat expansion in the HTT gene. Although HD is frequently complicated by depression, it is still unknown to what extent common HTT CAG repeat size variations in the normal range could affect depression risk in the general population. Using binary logistic regression, we assessed the association between HTT CAG repeat size and depression risk in two well-characterized Dutch cohorts─the Netherlands Study of Depression and Anxiety and the Netherlands Study of Depression in Older Persons─including 2165 depressed and 1058 non-depressed persons. In both cohorts, separately as well as combined, there was a significant non-linear association between the risk of lifetime depression and HTT CAG repeat size in which both relatively short and relatively large alleles were associated with an increased risk of depression (β = -0.292 and β = 0.006 for the linear and the quadratic term, respectively; both P < 0.01 after adjustment for the effects of sex, age, and education level). The odds of lifetime depression were lowest in persons with a HTT CAG repeat size of 21 (odds ratio: 0.71, 95% confidence interval: 0.52 to 0.98) compared to the average odds in the total cohort. In conclusion, lifetime depression risk was higher with both relatively short and relatively large HTT CAG repeat sizes in the normal range. Our study provides important proof-of-principle that repeat polymorphisms can act as hitherto unappreciated but complex genetic modifiers of depression.

2 Review [Emotional scars: impact of childhood trauma on the development of depressive and anxiety disorders later in life]. 2017

Hovens, J G F M / Giltay, E J / van Hemert, A M / Penninx, B W J H. · ·Tijdschr Psychiatr · Pubmed #28593622.

ABSTRACT: BACKGROUND: Childhood trauma and negative life events in childhood are risk factors for the development of anxiety and depressive disorders in adulthood.
AIM: To increase our understanding of the specific associations between trauma and negative life events in childhood and the development and course of anxiety and depressive disorders in adulthood.
METHOD: Our research findings are based on data from the Netherlands Study of Depression and Anxiety (NESDA). In our article we report on two cross-sectional and three prospective studies.
RESULTS: All domains of childhood trauma are risk factors for the development of anxiety and/or depressive disorders in adulthood. Emotional neglect is the main independent predictor of the occurrence and the course of anxiety and depressive disorders. Certain personality characteristics and more unfavorable clinical factors play an important role in mediating the relationship between childhood trauma and the course of anxiety and depressive disorders later in life.
CONCLUSION: Not only does childhood trauma increase an individual's vulnerability to the development of anxiety and depressive disorders, it is also associated with a more serious and more chronic course of these disorders. Our studies have provided new insights into the underlying mechanism that links childhood trauma and anxiety and later anxiety depressive disorders. Consequently, we feel justified in making some recommendations with regards to clinical practice and public health interventions.

3 Article Prediction of prolonged treatment course for depressive and anxiety disorders in an outpatient setting: The Leiden routine outcome monitoring study. 2019

Boer, Suzanne / Dekkers, Olaf M / Cessie, Saskia le / Carlier, Ingrid Ve / van Hemert, Albert M. ·Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands; Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands; Department of Medical Decision Making, Leiden University Medical Centre, Leiden, The Netherlands. Electronic address: s.boer@lumc.nl. · Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands. · Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands; Department of Medical Statistics and Bio-informatics, Leiden University Medical Centre, Leiden, The Netherlands. · Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands. ·J Affect Disord · Pubmed #30658244.

ABSTRACT: OBJECTIVE: The aim of this study was to improve clinical identification of patients with a prolonged treatment course for depressive and anxiety disorders early in treatment. METHOD: We conducted a cohort study in 1.225 adult patients with a depressive or anxiety disorders in psychiatric specialty care setting between 2007 and 2011, with at least two Brief Symptom Inventory (BSI) assessments within 6 months. With logistic regression, we modelled baseline age, gender, ethnicity, education, marital status, housing situation, employment status, psychiatric comorbidity and both baseline and 1st follow-up BSI scores to predict prolonged treatment course (>2 years). Based on the regression coefficients, we present an easy to use risk prediction score. RESULTS: BSI at 1st follow-up proved to be a strong predictor for both depressive and anxiety disorders (OR = 2.17 (CI95% 1.73-2.74); OR = 2.52 (CI95% 1.86-3.23)). The final risk prediction score included BSI 1st follow-up and comorbid axis II disorder for depressive disorder, for anxiety disorders BSI 1st follow-up and age were included. For depressive disorders, for 28% of the patients with the highest scores, the positive predictive value for a prolonged treatment course was60% (sensitivity 0.38, specificity 0.81). For anxiety disorders, for 35% of the patients with the highest scores, the positive predictive value for a prolonged treatment course was 52% (sensitivity 0.55, specificity 0.75). CONCLUSIONS: A high level of symptoms at 2-6 months of follow-up is a strong predictor for prolonged treatment course. This facilitates early identification of patients at risk of a prolonged course of treatment; in a relatively easy way by a self-assessed symptom severity.

4 Article Comparing factor structures of depressed patients with and without suicidal ideation, a measurement invariance analysis. 2019

van Ballegooijen, Wouter / Eikelenboom, Merijn / Fokkema, Marjolein / Riper, Heleen / van Hemert, Albert M / Kerkhof, Ad J F M / Penninx, Brenda W J H / Smit, Jan H. ·Amsterdam UMC, Vrije Universiteit, Department of Psychiatry, Amsterdam, The Netherlands; GGZ inGeest Specialised Mental Health Care, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands; Vrije Universiteit Amsterdam, Section Clinical Psychology, Amsterdam, The Netherlands. Electronic address: w.van.ballegooijen@vu.nl. · Amsterdam UMC, Vrije Universiteit, Department of Psychiatry, Amsterdam, The Netherlands; GGZ inGeest Specialised Mental Health Care, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands. · Leiden University, Department of Methods & Statistics, Leiden, Netherlands. · Amsterdam UMC, Vrije Universiteit, Department of Psychiatry, Amsterdam, The Netherlands; GGZ inGeest Specialised Mental Health Care, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands; Vrije Universiteit Amsterdam, Section Clinical Psychology, Amsterdam, The Netherlands. · Leiden University Medical Centre, Department of Psychiatry, Leiden, Netherlands. · Vrije Universiteit Amsterdam, Section Clinical Psychology, Amsterdam, The Netherlands. ·J Affect Disord · Pubmed #30396056.

ABSTRACT: BACKGROUND: Suicidality could be associated with specific combinations of biological, social and psychological factors. Therefore, depressive episodes with suicidal ideation could be different from depressive episodes without suicidal ideation in terms of latent variable structures. METHODS: In this study we compared latent variable structures between suicidal and non-suicidal depressed patients using confirmatory factor analysis (CFA), following a measurement invariance test procedure. Patients (N = 919) suffering from major depressive disorder were selected from the Netherlands Study of Depression and Anxiety (NESDA) and split into a group that showed no symptoms of suicidal ideation (non-SI; N = 691) and a suicidal ideation (SI) group that had one or more symptoms of suicidal ideation (N = 228). Depression and anxiety symptoms were measured using the short form of the Mood and Anxiety Symptoms Questionnaire (MASQ-D30). RESULTS: CFA implied a difference in latent variable structures between the non-SI sample (CFI 0.957; RMSEA 0.041) and the SI sample (CFI 0.900; RMSEA 0.056). Subsequent multiple-group CFA showed violations of measurement invariance. The General distress and Anhedonic depression subscales were best indicated by hopelessness and lack of optimism in the SI sample and by dissatisfaction and not feeling lively in the non-SI sample. Overall, the SI sample had higher scores and lower inter-item correlations on the Anhedonic depression items. LIMITATIONS: We have included very mild cases of suicidal ideation in our SI sample. CONCLUSIONS: On a latent variable level, depression with suicidal ideation differs from depression without suicidal ideation. Results encourage further research into the symptom structure of depression among suicidal patients.

5 Article Comparative responsiveness of generic versus disorder-specific instruments for depression: An assessment in three longitudinal datasets. 2019

de Beurs, Edwin / Vissers, Ellen / Schoevers, Robert / Carlier, Ingrid V E / van Hemert, Albert M / Meesters, Ybe. ·Faculty of Clinical Psychology, Leiden University, Leiden, The Netherlands. · Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands. · Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands. ·Depress Anxiety · Pubmed #30188602.

ABSTRACT: BACKGROUND: Routine outcome monitoring (ROM) may enhance individual treatment and is also advocated as a means to compare the outcome of different treatment programs or providers. There is debate on the optimal instruments to be used for these separate tasks. METHODS: Three sets with longitudinal data from ROM were analyzed with correlational analysis and repeated measures ANOVAs, allowing for a head-to-head comparison of measures regarding their sensitivity to detect change. The responsiveness of three disorder-specific instruments, the Beck Depression Inventory, the Inventory of Depressive Symptoms, and the Mood and Anxiety Symptoms Questionnaire, was compared to three generic instruments, the Symptom Checklist (SCL-90), the Outcome Questionnaire (OQ-45), and the Brief Symptom Inventory, respectively. RESULTS: In two of the three datasets, disorder-specific measures were more responsive compared to the total score on generic instruments. Subscale scores for depression embedded within generic instruments are second best and almost match disorder-specific scales in responsiveness. No evidence of a desynchronous response on outcome measures was found. LIMITATIONS: The present study compares measures head-to-had, and responsiveness is not assessed against an external criterion, such as clinical recovery. DISCUSSION: Disorder-specific measures yield the most precise assessment for individual treatment and are recommended for clinical use. Generic measures may allow for comparisons across diagnostic groups and their embedded subscales approach the responsiveness of disorder-specific measures.

6 Article Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach. 2018

Dinga, Richard / Marquand, Andre F / Veltman, Dick J / Beekman, Aartjan T F / Schoevers, Robert A / van Hemert, Albert M / Penninx, Brenda W J H / Schmaal, Lianne. ·Department of Psychiatry and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands. · Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands. · Department of Neuroimaging, Institute of Psychiatry, King's College London, London, United Kingdom. · University Medical Center Groningen, Department of Psychiatry, Research School of Behavioural and Cognitive Neurosciences (BCN), University of Groningen, Groningen, The Netherlands. · Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands. · Department of Psychiatry and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands. lianne.schmaal@unimelb.edu.au. · Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, VIC, Australia. lianne.schmaal@unimelb.edu.au. · Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia. lianne.schmaal@unimelb.edu.au. ·Transl Psychiatry · Pubmed #30397196.

ABSTRACT: Many variables have been linked to different course trajectories of depression. These findings, however, are based on group comparisons with unknown translational value. This study evaluated the prognostic value of a wide range of clinical, psychological, and biological characteristics for predicting the course of depression and aimed to identify the best set of predictors. Eight hundred four unipolar depressed patients (major depressive disorder or dysthymia) patients were assessed on a set involving 81 demographic, clinical, psychological, and biological measures and were clinically followed-up for 2 years. Subjects were grouped according to (i) the presence of a depression diagnosis at 2-year follow-up (yes n = 397, no n = 407), and (ii) three disease course trajectory groups (rapid remission, n = 356, gradual improvement n = 273, and chronic n = 175) identified by a latent class growth analysis. A penalized logistic regression, followed by tight control over type I error, was used to predict depression course and to evaluate the prognostic value of individual variables. Based on the inventory of depressive symptomatology (IDS), we could predict a rapid remission course of depression with an AUROC of 0.69 and 62% accuracy, and the presence of an MDD diagnosis at follow-up with an AUROC of 0.66 and 66% accuracy. Other clinical, psychological, or biological variables did not significantly improve the prediction. Among the large set of variables considered, only the IDS provided predictive value for course prediction on an individual level, although this analysis represents only one possible methodological approach. However, accuracy of course prediction was moderate at best and further improvement is required for these findings to be clinically useful.

7 Article [Complete recovery from depression is the exception rather than the rule: prognosis of depression beyond diagnostic boundaries]. 2018

Verhoeven, Josine E / Verduijn, Judith / Schoevers, Robert A / van Hemert, Albert M / Beekman, Aartjan T F / Penninx, Brenda W J H. ·VUmc, afd. Psychiatrie/GGZ inGeest, Amsterdam. · Contact: J.E. Verhoeven (j.verhoeven@ggzingeest.nl). · UMCG-Rijksuniversiteit Groningen, afd. Psychiatrie. · LUMC, afd. Psychiatrie, Leiden. ·Ned Tijdschr Geneeskd · Pubmed #30306757.

ABSTRACT: OBJECTIVE: To investigate whether the course of depression changes when (a) follow-up duration is longer and (b) in addition to depression other mood and anxiety disorders are considered as outcome measures. DESIGN: Longitudinal observational cohort study. METHOD: We selected patients from the Netherlands Study of Depression and Anxiety (NESDA) who had active depression at baseline (n=903) and for whom data from the 2, 4 and/or 6 year measurements were available. Using DSM-IV diagnoses and data from the 'Life chart interview', we divided participants in one of the following four course categories: (1) recovered (no diagnosis at 2-year measurement or later), (2) recurring without chronic episodes, (3) recurring with chronic episodes or (4) consistent chronic depression since baseline. We looked at the distribution of patients over the course categories from a short, diagnostically narrow perspective (over 2 years, only looking at depression) to a long, diagnostically broad perspective (over 6 years, looking at depression, dysthymia, hypomania, mania and anxiety). RESULTS: In the short, diagnostically narrow perspective, 58% of participants had recovered and 21% met the criteria for a chronic episode. In the long, diagnostically broad perspective however, only 17% had recovered while 55% had chronic episodes. CONCLUSION: Monitoring patients with depression over a longer period and with broader outcome measures (depression and related psychiatric disorders belonging to the mood disorder spectrum) shows that the course of depression is unfavourable and chronic for the majority. Conceptualising depression as a defined episodic disorder underestimates the severity of the prognosis for many patients and, as a consequence, the type of care indicated.

8 Article Repetitive negative thinking as a predictor of depression and anxiety: A longitudinal cohort study. 2018

Spinhoven, Philip / van Hemert, Albert M / Penninx, Brenda W. ·Leiden University, Institute of Psychology, Leiden, The Netherlands; Leiden University Medical Center, Department of Psychiatry, Leiden, The Netherlands. Electronic address: Spinhoven@FSW.LeidenUniv.NL. · Leiden University Medical Center, Department of Psychiatry, Leiden, The Netherlands. · Leiden University Medical Center, Department of Psychiatry, Leiden, The Netherlands; VU University Medical Center, Department of Psychiatry Amsterdam, The Netherlands. ·J Affect Disord · Pubmed #30138805.

ABSTRACT: BACKGROUND: Repetitive Negative Thinking (RNT) is assumed to be a transdiagnostic proximal risk factor in depression and anxiety. We examined the prospective relations of disorder-dependent as well as disorder-independent measures of RNT with depression and anxiety outcomes. METHODS: In a prospective cohort study, 1972 adults completed a 3-year follow-up period (attrition = 12.6%). DSM-IV diagnoses were assessed with the CIDI, symptom severity with the IDS and BAI, and RNT with measures for perseverative thinking (PTQ), rumination (LEIDS-R) and worry (PWQ). RESULTS: The common dimension of our RNT measurements (according to Confirmatory Factor Analysis) was significantly associated with comorbidity among depressive and among anxiety disorders, severity of depressive and anxiety symptoms, as well as persistence and relapse of depressive and anxiety disorders. Additionally, a specific factor for rumination predicted comorbidity of depressive disorders, comorbidity of anxiety disorders and relapse of depressive disorder, while a specific factor for worry predicted comorbidity of anxiety disorders and relapse of anxiety disorders, although to a lesser extent than general RNT. LIMITATIONS: The present study relied solely on self-report measures of RNT and controlling for baseline demographic and clinical variables greatly attenuated the predictive value of RNT. DISCUSSION: Disorder-independent RNT may be a similar underlying process present across depressive and anxiety disorders. It seems more important than the representation of this process in disorder-specific cognitive content such as rumination in depression and worry in anxiety. RNT as a pathological trait deserves more attention in clinical diagnosis and the transdiagnostic treatment of comorbid depression and anxiety in particular.

9 Article Clinical and sociodemographic associations with treatment selection in major depression. 2018

Rodenburg-Vandenbussche, S / Carlier, I V E / van Vliet, I M / van Hemert, A M / Stiggelbout, A M / Zitman, F G. ·Department of Psychiatry, Leiden University Medical Centre, Leiden, the Netherlands. Electronic address: S.Rodenburg-Vandenbussche@LUMC.nl. · Department of Psychiatry, Leiden University Medical Centre, Leiden, the Netherlands. · Department of Medical Decision Making, Leiden University Medical Centre, Leiden, the Netherlands. ·Gen Hosp Psychiatry · Pubmed #30048764.

ABSTRACT: OBJECTIVE: To investigate treatment selection in a naturalistic sample of MDD outpatients and the factors influencing treatment selection in specialized psychiatric care. METHOD: Multinomial Logistic Regression analysis investigated associations between treatment selection and patients' sociodemographic and clinical characteristics, using retrospective chart review data and Routine Outcome Monitoring (ROM) data of MDD outpatients. RESULTS: Of the patients included for analyses (N = 263), 34% received psychotherapy, 32% received an antidepressant (AD) and 35% received a combination. Men were more likely than women to receive AD with reference to psychotherapy (OR CONCLUSION: AD prescriptions in primary care, severity and gender influenced treatment selection for depressive disorders in secondary psychiatric care. Other factors such as the accessibility of treatment and patient preferences may have played a role in treatment selection in this setting and need further investigation.

10 Article Elevated salivary alpha-amylase levels at awakening in patients with depression. 2018

Bauduin, S E E C / van Noorden, M S / van der Werff, S J A / de Leeuw, M / van Hemert, A M / van der Wee, N J A / Giltay, E J. ·Department of Psychiatry, Leiden University Medical Center (LUMC), The Netherlands. Electronic address: S.E.E.C.Bauduin@lumc.nl. · Department of Psychiatry, Leiden University Medical Center (LUMC), The Netherlands. · Department of Psychiatry, Leiden University Medical Center (LUMC), The Netherlands; Psychiatric Outpatient Clinic, GGZ Rivierduinen, The Netherlands. ·Psychoneuroendocrinology · Pubmed #30005283.

ABSTRACT: BACKGROUND: Specific Major Depressive Disorder (MDD) biomarkers could help improve our understanding of MDD pathophysiology and aid in the refinement of current MDD criteria. While salivary cortisol (SC) can differentiate between healthy controls and patients with psychiatric disorders, salivary alpha amylase (sAA), may be a putative candidate biomarker for MDD specifically. METHODS: In a naturalistic cohort of consecutive out-patients and healthy controls, sAA and SC were determined in 833 participants (97 MDD patients, 142 patients with other mood, anxiety, and/or somatoform (MAS-) disorders, and 594 healthy controls). Samples were collected at 7 different time points (at awakening, after 30, 45, and 60 min, at 10:00 p.m., at 11:00 p.m., and at awakening on day 2). RESULTS: The mean age of the sample was 43.8 years (SD = 12.9; 63.9% female). Concerning sAA, MDD patients had higher sAA levels upon awakening on two consecutive days (p = 0.04, p = 0.01 respectively), as well as a higher area under the curve with respect to the increase (AUCi; p = 0.04) in comparison to both controls and the other MAS-disorders group. Regarding SC, mean levels of evening SC were elevated in MDD patients (p = 0.049) in comparison to both controls and the other MAS-disorders group. SC values on day 2 after ingestion of dexamethasone were elevated in both MDD patients and the other MAS-disorders group (p = 0.04, p = 0.047 respectively). CONCLUSIONS: sAA at awakening and not cortisol differentiates MDD from other psychiatric disorders in outpatients. This suggests that sAA may be a valuable candidate biomarker specifically for MDD.

11 Article Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. 2018

Wray, Naomi R / Ripke, Stephan / Mattheisen, Manuel / Trzaskowski, Maciej / Byrne, Enda M / Abdellaoui, Abdel / Adams, Mark J / Agerbo, Esben / Air, Tracy M / Andlauer, Till M F / Bacanu, Silviu-Alin / Bækvad-Hansen, Marie / Beekman, Aartjan F T / Bigdeli, Tim B / Binder, Elisabeth B / Blackwood, Douglas R H / Bryois, Julien / Buttenschøn, Henriette N / Bybjerg-Grauholm, Jonas / Cai, Na / Castelao, Enrique / Christensen, Jane Hvarregaard / Clarke, Toni-Kim / Coleman, Jonathan I R / Colodro-Conde, Lucía / Couvy-Duchesne, Baptiste / Craddock, Nick / Crawford, Gregory E / Crowley, Cheynna A / Dashti, Hassan S / Davies, Gail / Deary, Ian J / Degenhardt, Franziska / Derks, Eske M / Direk, Nese / Dolan, Conor V / Dunn, Erin C / Eley, Thalia C / Eriksson, Nicholas / Escott-Price, Valentina / Kiadeh, Farnush Hassan Farhadi / Finucane, Hilary K / Forstner, Andreas J / Frank, Josef / Gaspar, Héléna A / Gill, Michael / Giusti-Rodríguez, Paola / Goes, Fernando S / Gordon, Scott D / Grove, Jakob / Hall, Lynsey S / Hannon, Eilis / Hansen, Christine Søholm / Hansen, Thomas F / Herms, Stefan / Hickie, Ian B / Hoffmann, Per / Homuth, Georg / Horn, Carsten / Hottenga, Jouke-Jan / Hougaard, David M / Hu, Ming / Hyde, Craig L / Ising, Marcus / Jansen, Rick / Jin, Fulai / Jorgenson, Eric / Knowles, James A / Kohane, Isaac S / Kraft, Julia / Kretzschmar, Warren W / Krogh, Jesper / Kutalik, Zoltán / Lane, Jacqueline M / Li, Yihan / Li, Yun / Lind, Penelope A / Liu, Xiaoxiao / Lu, Leina / MacIntyre, Donald J / MacKinnon, Dean F / Maier, Robert M / Maier, Wolfgang / Marchini, Jonathan / Mbarek, Hamdi / McGrath, Patrick / McGuffin, Peter / Medland, Sarah E / Mehta, Divya / Middeldorp, Christel M / Mihailov, Evelin / Milaneschi, Yuri / Milani, Lili / Mill, Jonathan / Mondimore, Francis M / Montgomery, Grant W / Mostafavi, Sara / Mullins, Niamh / Nauck, Matthias / Ng, Bernard / Nivard, Michel G / Nyholt, Dale R / O'Reilly, Paul F / Oskarsson, Hogni / Owen, Michael J / Painter, Jodie N / Pedersen, Carsten Bøcker / Pedersen, Marianne Giørtz / Peterson, Roseann E / Pettersson, Erik / Peyrot, Wouter J / Pistis, Giorgio / Posthuma, Danielle / Purcell, Shaun M / Quiroz, Jorge A / Qvist, Per / Rice, John P / Riley, Brien P / Rivera, Margarita / Saeed Mirza, Saira / Saxena, Richa / Schoevers, Robert / Schulte, Eva C / Shen, Ling / Shi, Jianxin / Shyn, Stanley I / Sigurdsson, Engilbert / Sinnamon, Grant B C / Smit, Johannes H / Smith, Daniel J / Stefansson, Hreinn / Steinberg, Stacy / Stockmeier, Craig A / Streit, Fabian / Strohmaier, Jana / Tansey, Katherine E / Teismann, Henning / Teumer, Alexander / Thompson, Wesley / Thomson, Pippa A / Thorgeirsson, Thorgeir E / Tian, Chao / Traylor, Matthew / Treutlein, Jens / Trubetskoy, Vassily / Uitterlinden, André G / Umbricht, Daniel / Van der Auwera, Sandra / van Hemert, Albert M / Viktorin, Alexander / Visscher, Peter M / Wang, Yunpeng / Webb, Bradley T / Weinsheimer, Shantel Marie / Wellmann, Jürgen / Willemsen, Gonneke / Witt, Stephanie H / Wu, Yang / Xi, Hualin S / Yang, Jian / Zhang, Futao / Anonymous9151162 / Anonymous9161162 / Arolt, Volker / Baune, Bernhard T / Berger, Klaus / Boomsma, Dorret I / Cichon, Sven / Dannlowski, Udo / de Geus, E C J / DePaulo, J Raymond / Domenici, Enrico / Domschke, Katharina / Esko, Tõnu / Grabe, Hans J / Hamilton, Steven P / Hayward, Caroline / Heath, Andrew C / Hinds, David A / Kendler, Kenneth S / Kloiber, Stefan / Lewis, Glyn / Li, Qingqin S / Lucae, Susanne / Madden, Pamela F A / Magnusson, Patrik K / Martin, Nicholas G / McIntosh, Andrew M / Metspalu, Andres / Mors, Ole / Mortensen, Preben Bo / Müller-Myhsok, Bertram / Nordentoft, Merete / Nöthen, Markus M / O'Donovan, Michael C / Paciga, Sara A / Pedersen, Nancy L / Penninx, Brenda W J H / Perlis, Roy H / Porteous, David J / Potash, James B / Preisig, Martin / Rietschel, Marcella / Schaefer, Catherine / Schulze, Thomas G / Smoller, Jordan W / Stefansson, Kari / Tiemeier, Henning / Uher, Rudolf / Völzke, Henry / Weissman, Myrna M / Werge, Thomas / Winslow, Ashley R / Lewis, Cathryn M / Levinson, Douglas F / Breen, Gerome / Børglum, Anders D / Sullivan, Patrick F / Anonymous9171162. ·Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia. naomi.wray@uq.edu.au. · Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. naomi.wray@uq.edu.au. · Medical and Population Genetics, Broad Institute, Cambridge, MA, USA. · Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. · Department of Psychiatry and Psychotherapy, Universitätsmedizin Berlin Campus Charité Mitte, Berlin, Germany. · Department of Biomedicine, Aarhus University, Aarhus, Denmark. · iSEQ, Centre for Integrative Sequencing, Aarhus University, Aarhus, Denmark. · iPSYCH, Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark. · Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden. · Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia. · Department of Biological Psychology and EMGO+ Institute for Health and Care Research, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. · Division of Psychiatry, University of Edinburgh, Edinburgh, UK. · Centre for Integrated Register-Based Research, Aarhus University, Aarhus, Denmark. · National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark. · Discipline of Psychiatry, University of Adelaide, Adelaide, South Australia, Australia. · Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany. · Munich Cluster for Systems Neurology (SyNergy), Munich, Germany. · Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA. · Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark. · Department of Psychiatry, Vrije Universiteit Medical Center and GGZ inGeest, Amsterdam, The Netherlands. · Virginia Institute for Psychiatric and Behavior Genetics, Richmond, VA, USA. · Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA. · Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. · Department of Clinical Medicine, Translational Neuropsychiatry Unit, Aarhus University, Aarhus, Denmark. · Statistical Genomics and Systems Genetics, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK. · Human Genetics, Wellcome Trust Sanger Institute, Cambridge, UK. · Department of Psychiatry, University Hospital of Lausanne, Prilly, Switzerland. · MRC Social Genetic and Developmental Psychiatry Centre, King's College London, London, UK. · Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia. · Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. · Centre for Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia. · Psychological Medicine, Cardiff University, Cardiff, UK. · Center for Genomic and Computational Biology, Duke University, Durham, NC, USA. · Department of Pediatrics, Division of Medical Genetics, Duke University, Durham, NC, USA. · Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. · Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. · Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK. · Institute of Human Genetics, University of Bonn, Bonn, Germany. · Life & Brain Center, Department of Genomics, University of Bonn, Bonn, Germany. · Psychiatry, Dokuz Eylul University School of Medicine, Izmir, Turkey. · Epidemiology, Erasmus MC, Rotterdam, The Netherlands. · Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA. · Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA. · Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Massachusetts General Hospital, Boston, MA, USA. · Research, 23andMe, Inc., Mountain View, CA, USA. · Neuroscience and Mental Health, Cardiff University, Cardiff, UK. · Bioinformatics, University of British Columbia, Vancouver, British Columbia, Canada. · Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA. · Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA. · Department of Psychiatry (UPK), University of Basel, Basel, Switzerland. · Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland. · Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany. · Department of Psychiatry, Trinity College Dublin, Dublin, Ireland. · Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. · Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA. · Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia. · Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark. · Institute of Genetic Medicine, Newcastle University, Newcastle-upon-Tyne, UK. · University of Exeter Medical School, Exeter, UK. · Danish Headache Centre, Department of Neurology, Rigshospitalet, Glostrup, Denmark. · Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Capital Region of Denmark, Copenhagen, Denmark. · iPSYCH, Lundbeck Foundation Initiative for Psychiatric Research, Copenhagen, Denmark. · Brain and Mind Centre, University of Sydney, Sydney, New South Wales, Australia. · Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Germany. · Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche, Ltd, Basel, Switzerland. · Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA. · Statistics, Pfizer Global Research and Development, Groton, CT, USA. · Max Planck Institute of Psychiatry, Munich, Germany. · Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, USA. · Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, USA. · Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA. · Psychiatry and Behavioral Sciences, University of Southern California, Los Angeles, CA, USA. · Informatics Program, Boston Children's Hospital, Boston, MA, USA. · Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. · Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. · Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. · Department of Endocrinology at Herlev University Hospital, University of Copenhagen, Copenhagen, Denmark. · Swiss Institute of Bioinformatics, Lausanne, Switzerland. · Institute of Social and Preventive Medicine (IUMSP), University Hospital of Lausanne, Lausanne, Switzerland. · Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA. · Mental Health, NHS 24, Glasgow, UK. · Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK. · Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany. · Statistics, University of Oxford, Oxford, UK. · Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA. · School of Psychology and Counseling, Queensland University of Technology, Brisbane, Queensland, Australia. · Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Service, South Brisbane, Queensland, Australia. · Child Health Research Centre, University of Queensland, Brisbane, Queensland, Australia. · Estonian Genome Center, University of Tartu, Tartu, Estonia. · Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada. · Statistics, University of British Columbia, Vancouver, British Columbia, Canada. · DZHK (German Centre for Cardiovascular Research), partner site Greifswald, University Medicine, University Medicine Greifswald, Greifswald, Germany. · Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany. · Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia. · Humus, Reykjavik, Iceland. · MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK. · Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA. · Complex Trait Genetics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. · Clinical Genetics, Vrije Universiteit Medical Center, Amsterdam, The Netherlands. · Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA. · Solid Biosciences, Boston, MA, USA. · Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, MO, USA. · Department of Biochemistry and Molecular Biology II, Institute of Neurosciences, Center for Biomedical Research, University of Granada, Granada, Spain. · Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. · Department of Psychiatry and Psychotherapy, Medical Center of the University of Munich, Campus Innenstadt, Munich, Germany. · Institute of Psychiatric Phenomics and Genomics (IPPG), Medical Center of the University of Munich, Campus Innenstadt, Munich, Germany. · Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA. · Behavioral Health Services, Kaiser Permanente Washington, Seattle, WA, USA. · Faculty of Medicine, Department of Psychiatry, University of Iceland, Reykjavik, Iceland. · School of Medicine and Dentistry, James Cook University, Townsville, Queensland, Australia. · Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK. · deCODE Genetics/Amgen, Inc., Reykjavik, Iceland. · Psychiatry and Human Behavior, University of Mississippi Medical Center, Jackson, MS, USA. · College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK. · Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany. · Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany. · KG Jebsen Centre for Psychosis Research, Norway Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway. · Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA. · Medical Genetics Section, CGEM, IGMM, University of Edinburgh, Edinburgh, UK. · Clinical Neurosciences, University of Cambridge, Cambridge, UK. · Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. · Roche Pharmaceutical Research and Early Development, Neuroscience, Ophthalmology and Rare Diseases Discovery and Translational Medicine Area, Roche Innovation Center Basel, F. Hoffmann-La Roche, Ltd, Basel, Switzerland. · Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany. · Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands. · Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA. · Computational Sciences Center of Emphasis, Pfizer Global Research and Development, Cambridge, MA, USA. · Department of Psychiatry, University of Münster, Munster, Germany. · Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany. · Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, Switzerland. · Amsterdam Public Health Institute, Vrije Universiteit Medical Center, Amsterdam, The Netherlands. · Centre for Integrative Biology, Università degli Studi di Trento, Trento, Italy. · Department of Psychiatry and Psychotherapy, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany. · Psychiatry, Kaiser Permanente Northern California, San Francisco, CA, USA. · MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK. · Centre for Addiction and Mental Health, Toronto, Ontario, Canada. · Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada. · Division of Psychiatry, University College London, London, UK. · Neuroscience Therapeutic Area, Janssen Research and Development, LLC, Titusville, NJ, USA. · Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia. · Psychosis Research Unit, Aarhus University Hospital, Risskov, Aarhus, Denmark. · Institute of Translational Medicine, University of Liverpool, Liverpool, UK. · Mental Health Center Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark. · Human Genetics and Computational Biomedicine, Pfizer Global Research and Development, Groton, CT, USA. · Psychiatry, Harvard Medical School, Boston, MA, USA. · Psychiatry, University of Iowa, Iowa City, IA, USA. · Human Genetics Branch, NIMH Division of Intramural Research Programs, Bethesda, MD, USA. · Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany. · Faculty of Medicine, University of Iceland, Reykjavik, Iceland. · Child and Adolescent Psychiatry, Erasmus MC, Rotterdam, The Netherlands. · Psychiatry, Erasmus MC, Rotterdam, The Netherlands. · Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada. · Division of Epidemiology, New York State Psychiatric Institute, New York, NY, USA. · Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. · Human Genetics and Computational Biomedicine, Pfizer Global Research and Development, Cambridge, MA, USA. · Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. · Department of Medical and Molecular Genetics, King's College London, London, UK. · Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA. · NIHR BRC for Mental Health, King's College London, London, UK. · Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. pfsulliv@med.unc.edu. · Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. pfsulliv@med.unc.edu. · Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. pfsulliv@med.unc.edu. ·Nat Genet · Pubmed #29700475.

ABSTRACT: Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.

12 Article Reconsidering the prognosis of major depressive disorder across diagnostic boundaries: full recovery is the exception rather than the rule. 2017

Verduijn, Judith / Verhoeven, Josine E / Milaneschi, Yuri / Schoevers, Robert A / van Hemert, Albert M / Beekman, Aartjan T F / Penninx, Brenda W J H. ·Department of Psychiatry, Amsterdam Public Health Research Institute, VU University Medical Center, PO Box 74077, 1070 BB, Amsterdam, The Netherlands. · Department of Psychiatry, Amsterdam Public Health Research Institute, VU University Medical Center, PO Box 74077, 1070 BB, Amsterdam, The Netherlands. j.verhoeven@ggzingeest.nl. · Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. · Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands. ·BMC Med · Pubmed #29228943.

ABSTRACT: BACKGROUND: Major depressive disorder (MDD) is often handled as an episodic and isolated disorder, resulting in an optimistic view about its prognosis. Herein, we test the idea that the prognosis of MDD changes if we vary the perspective in terms of (1) a longer time frame and (2) a broader diagnostic conceptualisation including dysthymia, (hypo)mania and anxiety disorders as relevant outcomes. METHODS: Patients with current MDD at baseline (n = 903) and available 2-, 4-, and/or 6-year follow-up assessments were selected from the Netherlands Study of Depression and Anxiety, a psychiatric cohort study. Combining psychiatric DSM-IV-based diagnoses and life-chart data, patient course trajectories were classified as (1) recovered (no diagnoses at 2-year follow-up or thereafter), (2) recurrent without chronic episodes, (3) recurrent with chronic episodes or (4) consistently chronic since baseline. A chronic episode was defined as having a current diagnosis at the follow-up assessment and consistent symptoms over 2 years. Proportions of course trajectories were provided moving from a short, narrow perspective (2-year follow-up, considering only MDD diagnosis) to a long, broad perspective (6-year follow-up, including MDD, dysthymia, (hypo)mania and anxiety diagnoses). RESULTS: With the short, narrow perspective, the recovery rate was 58% and 21% had a chronic episode. However, in the long, broad perspective the recovery rate was reduced to 17%, while 55% of the patients experienced chronic episodes. CONCLUSIONS: Results from a long and rigorous follow-up in a large cohort suggests that most MDD patients have an unfavourable prognosis. Longer follow-up and broader diagnostic conceptualisation show that the majority of patients have a disabling and chronic disorder. Conceptualising and handling MDD as a narrowly defined and episodic disorder may underestimate the prognosis of the majority of depressed patients and, consequently, the type of care that is appropriate.

13 Article The role of anxious distress in immune dysregulation in patients with major depressive disorder. 2017

Gaspersz, Roxanne / Lamers, Femke / Wittenberg, Gayle / Beekman, Aartjan T F / van Hemert, Albert M / Schoevers, Robert A / Penninx, Brenda W J H. ·Department of Psychiatry, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands. r.gaspersz@ggzingeest.nl. · Department of Psychiatry, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands. · Janssen Research & Development, LLC, Titusville, NJ, USA. · Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands. · Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. ·Transl Psychiatry · Pubmed #29217840.

ABSTRACT: Although depression with anxious distress appears to be a clinically relevant subtype of major depressive disorder (MDD), whether it involves specific pathophysiology remains unclear. Inflammation has been implicated, but not comprehensively studied. We examined within a large MDD sample whether anxious distress and related anxiety features are associated with differential basal inflammation and innate cytokine production capacity. Data are from 1078 MDD patients from the Netherlands Study of Depression and Anxiety. In addition to the DSM-5 anxious distress specifier, we studied various dimensional anxiety scales (e.g. Inventory of Depressive Symptomatology anxiety arousal subscale [IDS-AA], Beck Anxiety Inventory [BAI], Mood and Anxiety Symptoms Questionnaire Anxious Arousal scale [MASQ-AA]). The specifier was constructed using five self-report items from the IDS and BAI. Basal inflammatory markers included C-reactive protein (CRP), interleukin (IL)-6 and tumor necrosis factor (TNF)-α. Innate production capacity was assessed by 13 lipopolysaccharide (LPS)-stimulated inflammatory markers. Basal and LPS-stimulated inflammation index scores were created. Basal inflammation was not associated with anxious distress (prevalence = 54.3%) in MDD patients, except for a modest positive association for BAI score. However, anxious distress was associated with higher LPS-stimulated levels (interferon-γ, IL-6, monocyte chemotactic protein (MCP)-1, macrophage inflammatory protein (MIP)-1α, matrix metalloproteinase (MMP)-2, TNF-α, LPS-stimulated index). Other anxiety indicators (anxious distress specifier score, BAI, MASQ-AA) were also associated with increased innate production capacity. Within a large MDD sample, the anxious distress specifier was associated with increased innate cytokine production capacity but not with basal inflammation. Results from dimensional anxiety indicators largely confirm these results. These findings provide new insight into the pathophysiology of anxious depression.

14 Article Large normal-range TBP and ATXN7 CAG repeat lengths are associated with increased lifetime risk of depression. 2017

Gardiner, S L / van Belzen, M J / Boogaard, M W / van Roon-Mom, W M C / Rozing, M P / van Hemert, A M / Smit, J H / Beekman, A T F / van Grootheest, G / Schoevers, R A / Oude Voshaar, R C / Comijs, H C / Penninx, B W J H / van der Mast, R C / Roos, R A C / Aziz, N A. ·Department of Neurology, Leiden University Medical Centre, Leiden, The Netherlands. · Department of Human Genetics, Leiden University Medical Centre, Leiden, The Netherlands. · Department of Clinical Genetics, Leiden University Medical Centre, Leiden, The Netherlands. · Centre for Healthy Ageing/Department of Public Health, Section of Social Medicine, University of Copenhagen, Copenhagen, Denmark. · Department of Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands. · Department of Psychiatry, Amsterdam Public Health Research Institute and Neuroscience Campus Amsterdam, VU University Medical Centre/GGZ inGeest, Amsterdam, The Netherlands. · Department of Psychiatry, University of Groningen, University Medical Centre Groningen, Research School Cognitive Behavioural Neuroscience, Groningen, The Netherlands. · Department of Psychiatry, Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Antwerp, Belgium. ·Transl Psychiatry · Pubmed #28585930.

ABSTRACT: Depression is one of the most prevalent and debilitating psychiatric disorders worldwide. Recently, we showed that both relatively short and relatively long cytosine-adenine-guanine (CAG) repeats in the huntingtin gene (HTT) are associated with an increased risk of lifetime depression. However, to what extent the variations in CAG repeat length in the other eight polyglutamine disease-associated genes (PDAGs) are associated with depression is still unknown. We determined the CAG repeat sizes of ATXN1, ATXN2, ATXN3, CACNA1A, ATXN7, TBP, ATN1 and AR in two well-characterized Dutch cohorts-the Netherlands Study of Depression and Anxiety and the Netherlands Study of Depression in Older Persons-including 2165 depressed and 1058 non-depressed individuals-aged 18-93 years. The association between PDAG CAG repeat size and the risk for depression was assessed via binary logistic regression. We found that the odds ratio (OR) for lifetime depression was significantly higher for individuals with >10, compared with subjects with ≤10, CAG repeats in both ATXN7 alleles (OR=1.90, confidence interval (CI) 1.26-2.85). For TBP we found a similar association: A CAG repeat length exceeding the median in both alleles was associated with an increased risk for lifetime depression (OR=1.33, CI 1.00-1.76). In conclusion, we observed that carriers of either ATXN7 or TBP alleles with relatively large CAG repeat sizes in both alleles had a substantially increased risk of lifetime depression. Our findings provide critical evidence for the notion that repeat polymorphisms can act as complex genetic modifiers of depression.

15 Article Genetic effects influencing risk for major depressive disorder in China and Europe. 2017

Bigdeli, T B / Ripke, S / Peterson, R E / Trzaskowski, M / Bacanu, S-A / Abdellaoui, A / Andlauer, T F M / Beekman, A T F / Berger, K / Blackwood, D H R / Boomsma, D I / Breen, G / Buttenschøn, H N / Byrne, E M / Cichon, S / Clarke, T-K / Couvy-Duchesne, B / Craddock, N / de Geus, E J C / Degenhardt, F / Dunn, E C / Edwards, A C / Fanous, A H / Forstner, A J / Frank, J / Gill, M / Gordon, S D / Grabe, H J / Hamilton, S P / Hardiman, O / Hayward, C / Heath, A C / Henders, A K / Herms, S / Hickie, I B / Hoffmann, P / Homuth, G / Hottenga, J-J / Ising, M / Jansen, R / Kloiber, S / Knowles, J A / Lang, M / Li, Q S / Lucae, S / MacIntyre, D J / Madden, P A F / Martin, N G / McGrath, P J / McGuffin, P / McIntosh, A M / Medland, S E / Mehta, D / Middeldorp, C M / Milaneschi, Y / Montgomery, G W / Mors, O / Müller-Myhsok, B / Nauck, M / Nyholt, D R / Nöthen, M M / Owen, M J / Penninx, B W J H / Pergadia, M L / Perlis, R H / Peyrot, W J / Porteous, D J / Potash, J B / Rice, J P / Rietschel, M / Riley, B P / Rivera, M / Schoevers, R / Schulze, T G / Shi, J / Shyn, S I / Smit, J H / Smoller, J W / Streit, F / Strohmaier, J / Teumer, A / Treutlein, J / Van der Auwera, S / van Grootheest, G / van Hemert, A M / Völzke, H / Webb, B T / Weissman, M M / Wellmann, J / Willemsen, G / Witt, S H / Levinson, D F / Lewis, C M / Wray, N R / Flint, J / Sullivan, P F / Kendler, K S. ·Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA. · Department of Psychiatry, Charite Universitatsmedizin Berlin Campus Benjamin Franklin, Berlin, Germany. · Medical and Population Genetics, Broad Institute, Cambridge, MA, USA. · Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. · Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia. · Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia. · Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. · Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany. · Munich Cluster for Systems Neurology (SyNergy), Munich, Germany. · Department of Psychiatry, VU University Medical Center and GGZ inGeest, Amsterdam, The Netherlands. · Institute of Epidemiology and Social Medicine, University of Muenster, Münster, Germany. · Division of Psychiatry, University of Edinburgh, Edinburgh, UK. · King's College London, NIHR BRC for Mental Health, London, UK. · King's College London, MRC Social Genetic and Developmental Psychiatry Centre, London, UK. · Department of Clinical Medicine, Translational Neuropsychiatry Unit, Aarhus University, Aarhus, Denmark. · Department of Biomedicine, University of Basel, Basel, Switzerland. · Division of Medical Genetics, University of Basel, Basel, Switzerland. · Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Jülich, Germany. · Institute of Human Genetics, University of Bonn, Bonn, Germany. · Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia. · Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia. · Department of Psychological Medicine, Cardiff University, Cardiff, UK. · EMGO+ Institute, VU University Medical Center, Amsterdam, The Netherlands. · Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany. · Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA. · Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA. · Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA. · Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA. · Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany. · Department of Psychiatry, Trinity College Dublin, Dublin, Ireland. · Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany. · Department of Psychiatry, Kaiser-Permanente Northern California, San Fransisco, CA, USA. · Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland. · Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK. · Department of Psychiatry, Washington University in Saint Louis School of Medicine, St Louis, MO, USA. · Division of Medical Genetics, Department of Biomedicine, University of Basel, Basel, Switzerland. · Brain and Mind Research Institute, University of Sydney, Sydney, NSW, Australia. · Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland. · Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst Moritz Arndt University Greifswald, Greifswald, Germany. · Max Planck Institute of Psychiatry, Munich, Germany. · Department of Psychiatry and The Behavioral Sciences, University of Southern California, Los Angeles, CA, USA. · Neuroscience Therapeutic Area, Janssen Research and Development, LLC, Titusville, NJ, USA. · School of Psychology, University of Queensland, Brisbane, QLD, Australia. · Department of Psychiatry, New York State Psychiatric Institute, Columbia University College of Physicians and Surgeons, New York, NY, USA. · Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK. · Institute for Molecular Biology, University of Queensland, Brisbane, QLD, Australia. · Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark. · Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK. · Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany. · Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia. · MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK. · Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, USA. · Department of Psychiatry, Harvard Medical School, Boston, MA, USA. · Medical Genetics Section, CGEM, IGMM, University of Edinburgh, Edinburgh, UK. · Department of Psychiatry, University of Iowa, Iowa, IA, USA. · Department of Psychiatry, Washington University in Saint Louis, St Louis, MO, USA. · Department of Human and Molecular Genetics, Virginia Commonwealth University School of Medicine, Richmond, VA, USA. · Department of Biochemistry and Molecular Biology II, Institute of Neurosciences, Center for Biomedical Research, University of Granada, Granada, Spain. · Department of Psychiatry, University of Groningen, University of Medical Center Groningen, Groningen, The Netherlands. · Institute of Psychiatric Phenomics and Genomics, Medical Center of the University of Munich, Campus Innenstadt, Munich, Germany. · Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, The Netherlands. · Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA. · Human Genetics Branch, NIMH Division of Intramural Research Programs, Bethesda, MD, USA. · Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA. · Division of Psychiatry, Group Health, Seattle, WA, USA. · Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany. · Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands. · Division of Epidemiology, New York State Psychiatric Institute, New York, NY, USA. · Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA. · Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA. · King's College London, Department of Medical and Molecular Genetics, London, UK. · Merton College, University of Oxford, Oxford, UK. · Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. · Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden. · Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. · Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. ·Transl Psychiatry · Pubmed #28350396.

ABSTRACT: Major depressive disorder (MDD) is a common, complex psychiatric disorder and a leading cause of disability worldwide. Despite twin studies indicating its modest heritability (~30-40%), extensive heterogeneity and a complex genetic architecture have complicated efforts to detect associated genetic risk variants. We combined single-nucleotide polymorphism (SNP) summary statistics from the CONVERGE and PGC studies of MDD, representing 10 502 Chinese (5282 cases and 5220 controls) and 18 663 European (9447 cases and 9215 controls) subjects. We determined the fraction of SNPs displaying consistent directions of effect, assessed the significance of polygenic risk scores and estimated the genetic correlation of MDD across ancestries. Subsequent trans-ancestry meta-analyses combined SNP-level evidence of association. Sign tests and polygenic score profiling weakly support an overlap of SNP effects between East Asian and European populations. We estimated the trans-ancestry genetic correlation of lifetime MDD as 0.33; female-only and recurrent MDD yielded estimates of 0.40 and 0.41, respectively. Common variants downstream of GPHN achieved genome-wide significance by Bayesian trans-ancestry meta-analysis (rs9323497; log

16 Article Anxious distress predicts subsequent treatment outcome and side effects in depressed patients starting antidepressant treatment. 2017

Gaspersz, Roxanne / Lamers, Femke / Kent, Justine M / Beekman, Aartjan T F / Smit, Johannes H / van Hemert, Albert M / Schoevers, Robert A / Penninx, Brenda W J H. ·Department of Psychiatry, EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands. Electronic address: r.gaspersz@ggzingeest.nl. · Department of Psychiatry, EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands. · Janssen Research & Development, LLC, Titusville, NJ, USA. · Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands. · University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, The Netherlands. · Department of Psychiatry, EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands; Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands; University of Groningen, University Medical Center Groningen, Department of Psychiatry, Groningen, The Netherlands. ·J Psychiatr Res · Pubmed #27693981.

ABSTRACT: Evidence has shown that the DSM-5 anxious distress specifier captures a clinically valid construct that predicts a worse clinical course. Although of importance for treatment planning and monitoring, however, the specifier's ability to predict treatment outcome is unknown. This is the first study to examine the ability of the DSM-5 anxious distress specifier to predict treatment response and side effects in depressed patients who recently initiated antidepressant treatment. Patients were from the Netherlands Study of Depression and Anxiety, an ongoing longitudinal cohort study. Baseline, 1-year and 2-year follow-up data were used from 149 patients (18-65 years) with current Major Depressive Disorder (MDD) who recently started adequately dosed antidepressant medication. Five self-report items were used to construct the DSM-5 anxious distress specifier. Treatment outcomes were depression severity after 1 year and 2 years, remission of MDD after 2 years and antidepressant side effects during treatment. For comparison, analyses were repeated for comorbid DSM-IV-based anxiety disorders as a predictor. In depressed patients who received antidepressant treatment, the anxious distress specifier (prevalence = 59.1%) significantly predicted higher severity (1 year: B = 1.94, P = 0.001; 2 years: B = 1.63, P = 0.001), lower remission rates (OR = 0.44, P = 0.0496) and greater frequency of side effects (≥4 vs. 0: OR = 2.74, P = 0.061). In contrast, the presence of comorbid anxiety disorders did not predict these treatment outcomes. The anxious distress specifier significantly predicts poorer treatment outcomes as shown by higher depression severity, lower remission rates, and greater frequency of antidepressant side effects in patients with MDD on adequate antidepressant treatment. Therefore, this simple 5-item specifier is of potential great clinical usefulness for treatment planning and monitoring in depressed patients.

17 Article Longitudinal Predictive Validity of the DSM-5 Anxious Distress Specifier for Clinical Outcomes in a Large Cohort of Patients With Major Depressive Disorder. 2017

Gaspersz, Roxanne / Lamers, Femke / Kent, Justine M / Beekman, Aartjan T F / Smit, Johannes H / van Hemert, Albert M / Schoevers, Robert A / Penninx, Brenda W J H. ·Department of Psychiatry, VU University Medical Center, Postbus 74077, 1070 BB Amsterdam, The Netherlands. r.gaspersz@ggzingeest.nl. · Department of Psychiatry, EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands. · Janssen Research & Development, LLC, Titusville, New Jersey, USA. · Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands. · Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands. ·J Clin Psychiatry · Pubmed #27035515.

ABSTRACT: OBJECTIVE: While the new DSM-5 anxious distress specifier is of great clinical importance, no evidence exists for its longitudinal predictive validity for clinical outcomes in patients with major depressive disorder (MDD). We examined the longitudinal validity of this specifier and validated it against DSM-IV-based comorbid anxiety disorder diagnoses. METHODS: Data are from 1,080 patients with current MDD at baseline (September 2004 to February 2007), of which 911 participated in the 2-year follow-up (September 2006 to April 2009). Patients are from the Netherlands Study of Depression and Anxiety, which is an ongoing longitudinal cohort study, and were sampled from the community, primary care, and outpatient specialized care settings. The specifier was constructed in the existing sample by 5 matching self-report items. Predictive outcomes were 2-year chronicity, time to remission of MDD, and functional disability. Discriminant performance and convergent validity of the specifier were also assessed. RESULTS: The specifier was present in 54.2% of the sample. The specifier significantly outperformed anxiety disorders in predicting chronicity (OR = 1.96, P < .001, vs OR = 1.11, P = .49), time to remission of MDD (HR = 0.75, P = .002, vs HR = 0.94, P = .55), and functional disability (B = 10.03, P < .001, vs B = 2.53, P = .07). The specifier significantly discriminated in clinical characteristics, had convergent validity for anxiety characteristics, and poorly overlapped with DSM-IV-based anxiety disorder diagnoses (Cohen κ = .09). CONCLUSIONS: The short anxious distress specifier outperforms DSM-IV-based anxiety disorder diagnoses as a longitudinal predictor for clinical outcomes in patients with MDD.

18 Article Assessing adherence to guidelines with administrative data in psychiatric outpatients. 2017

van Fenema, Esther / Giltay, Erik / van Noorden, Martijn / van Hemert, Albert / Zitman, Frans. ·Psychiatry, Leiden University Medical Center, Leiden, The Netherlands. ·J Eval Clin Pract · Pubmed #26223425.

ABSTRACT: RATIONALE, AIMS AND OBJECTIVES: To assess (feasibility) of adherence to treatment guidelines among outpatients with common mental disorders in a routine Dutch clinical outpatient setting for common mental disorders using administrative data. METHODS: In a retrospective cohort study, we analysed routinely collected administrative data of 5346 patients, treated for mood, anxiety or somatoform disorders with pharmacotherapy, psychotherapy or a combination of both. Available administrative data allowed assessment of guideline adherence with a disorder-independent set of five quality indicators, assessing psychotherapy, pharmacotherapy, a combination of both and routine outcome measurements (ROM) during diagnostic and therapeutic phases. Associations between the socio-demographic variables age, gender, clinical diagnosis and treatment type on the one hand and non-adherence to guidelines were tested using logistic regression analysis. RESULTS: Patients were aged 39.5 years (SD 13.0) on average. The majority of patients were treated with a combination of pharmacotherapy and psychotherapy (50.1%), followed by psychotherapy (44.2%) and pharmacotherapy (5.6%). The majority of patients were suffering from a mood disorder (50.0%), followed by anxiety (43.9%) and somatoform disorders (6.1%). A diagnosis of anxiety or somatoform disorder was associated with higher odds of suboptimal duration [odds ratio (OR): 1.55 and 1.82[ and suboptimal frequency of psychotherapeutic treatment (OR of 0.89 and 0.63), and absence of ROM in the diagnostic phase (ORs 1.31 and 1.36, respectively) compared with depressive disorders. No ROM in the diagnostic phase was also predicted for by increasing age (ORs for the age categories of 56 and older of 1.48). CONCLUSIONS: In this proof of principal study, we were able to assess some key indicators assessing adherence to clinical guidelines by using administrative data. Also, we could identify predictors of adherence with simple parameters available in every administrative data. Administrative data could help to monitor and aid guideline adherence in routine care, although quality may vary between settings.

19 Article The clinical effectiveness of concise cognitive behavioral therapy with or without pharmacotherapy for depressive and anxiety disorders; a pragmatic randomized controlled equivalence trial in clinical practice. 2016

Meuldijk, D / Carlier, I V E / van Vliet, I M / van Veen, T / Wolterbeek, R / van Hemert, A M / Zitman, F G. ·Leiden University Medical Center, Department of Psychiatry, Leiden, The Netherlands. Electronic address: d.meuldijk@lumc.nl. · Leiden University Medical Center, Department of Psychiatry, Leiden, The Netherlands. · Leiden University Medical Center, Department of Medical Statistics and Bioinformatics, Leiden, The Netherlands. ·Contemp Clin Trials · Pubmed #26762883.

ABSTRACT: BACKGROUND: Depressive and anxiety disorders contribute to a high disease burden. This paper investigates whether concise formats of cognitive behavioral- and/or pharmacotherapy are equivalent with longer standard care in the treatment of depressive and/or anxiety disorders in secondary mental health care. METHODS: A pragmatic randomized controlled equivalence trial was conducted at five Dutch outpatient Mental Healthcare Centers (MHCs) of the Regional Mental Health Provider (RMHP) 'Rivierduinen'. Patients (aged 18-65 years) with a mild to moderate anxiety and/or depressive disorder, were randomly allocated to concise or standard care. Data were collected at baseline, 3, 6 and 12 months by Routine Outcome Monitoring (ROM). Primary outcomes were the Brief Symptom Inventory (BSI) and the Web Screening Questionnaire (WSQ). We used Generalized Estimating Equations (GEE) to assess outcomes. RESULTS: Between March 2010 and December 2012, 182 patients, were enrolled (n=89 standard care; n=93 concise care). Both intention-to-treat and per-protocol analyses demonstrated equivalence of concise care and standard care at all time points. Severity of illness reduced, and both treatments improved patient's general health status and subdomains of quality of life. Moreover, in concise care, the beneficial effects started earlier. DISCUSSION: Concise care has the potential to be a feasible and promising alternative to longer standard secondary mental health care in the treatment of outpatients with a mild to moderate depressive and/or anxiety disorder. For future research, we recommend adhering more strictly to the concise treatment protocols to further explore the beneficial effects of the concise treatment. The study is registered in the Netherlands Trial Register, number NTR2590. Clinicaltrials.gov identifier: NCT01643642.

20 Article Simulating computer adaptive testing with the Mood and Anxiety Symptom Questionnaire. 2016

Flens, Gerard / Smits, Niels / Carlier, Ingrid / van Hemert, Albert M / de Beurs, Edwin. ·Foundation for Benchmarking Mental Health Care. · Research Institute of Child Development and Education, University of Amsterdam. · Department of Psychiatry, Leiden University Medical Centre. ·Psychol Assess · Pubmed #26691506.

ABSTRACT: In a post hoc simulation study (N = 3,597 psychiatric outpatients), we investigated whether the efficiency of the 90-item Mood and Anxiety Symptom Questionnaire (MASQ) could be improved for assessing clinical subjects with computerized adaptive testing (CAT). A CAT simulation was performed on each of the 3 MASQ subscales (Positive Affect, Negative Affect, and Somatic Anxiety). With the CAT simulation's stopping rule set at a high level of measurement precision, the results showed that patients' test administration can be shortened substantially; the mean decrease in items used for the subscales ranged from 56% up to 74%. Furthermore, the predictive utility of the CAT simulations was sufficient for all MASQ scales. The findings reveal that developing a MASQ CAT for clinical subjects is useful as it leads to more efficient measurement without compromising the reliability of the test outcomes. (PsycINFO Database Record

21 Article Childhood maltreatment, maladaptive personality types and level and course of psychological distress: A six-year longitudinal study. 2016

Spinhoven, Philip / Elzinga, Bernet M / Van Hemert, Albert M / de Rooij, Mark / Penninx, Brenda W. ·Section of Clinical Psychology, Leiden University, Leiden, The Netherlands; Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands. Electronic address: Spinhoven@FSW.LeidenUniv.NL. · Section of Clinical Psychology, Leiden University, Leiden, The Netherlands. · Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands. · Section of Methods and Statistics, Leiden University, Leiden, The Netherlands. · Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands; Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands; Department of Psychiatry/ EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, The Netherlands. ·J Affect Disord · Pubmed #26655119.

ABSTRACT: BACKGROUND: Childhood maltreatment and maladaptive personality are both cross-sectionally associated with psychological distress. It is unknown whether childhood maltreatment affects the level and longitudinal course of psychological distress in adults and to what extent this effect is mediated by maladaptive personality. METHODS: A sample of 2947 adults aged 18-65, consisting of healthy controls, persons with a prior history or current episode of depressive and/or anxiety disorders according to the Composite Interview Diagnostic Instrument were assessed in six waves at baseline (T0) and 1 (T1), 2 (T2), 4 (T4) and 6 years (T6) later. At each wave psychological distress was measured with the Inventory of Depressive Symptomatology, Beck Anxiety Inventory, and Fear Questionnaire. At T0 childhood maltreatment types were measured with a semi-structured interview (Childhood Trauma Interview) and personality traits with the NEO-Five Factor Inventory. RESULTS: Using latent variable analyses, we found that severity of childhood maltreatment (emotional neglect and abuse in particular) predicted higher initial levels of psychological distress and that this effect was mediated by maladaptive personality types. Differences in trajectories of distress between persons with varying levels of childhood maltreatment remained significant and stable over time. LIMITATIONS: Childhood maltreatment was assessed retrospectively and maladaptive personality types and level of psychological distress at study entry were assessed concurrently. CONCLUSIONS: Routine assessment of maladaptive personality types and possible childhood emotional maltreatment in persons with severe and prolonged psychological distress seems warranted to identify persons who may need a different or more intensive treatment.

22 Article CHILDHOOD MALTREATMENT AND THE COURSE OF DEPRESSIVE AND ANXIETY DISORDERS: THE CONTRIBUTION OF PERSONALITY CHARACTERISTICS. 2016

Hovens, Jacqueline G F M / Giltay, Erik J / van Hemert, Albert M / Penninx, Brenda W J H. ·Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands. · VU University Medical Center, Amsterdam, The Netherlands. · Department of Psychiatry, University Medical Center Groningen, Groningen, The Netherlands. ·Depress Anxiety · Pubmed #26418232.

ABSTRACT: BACKGROUND: We investigated the effect of childhood maltreatment on predicting the 4-year course of depressive and anxiety disorders and the possible mediating role of personality characteristics in the association between childhood maltreatment and illness course. METHODS: Longitudinal data in a large sample of participants with baseline depressive and/or anxiety disorders (n = 1,474, 18-65 years) were collected in the Netherlands Study of Depression and Anxiety. At baseline, childhood maltreatment was assessed with a semistructured interview. Personality trait questionnaires (Neuroticism-Extroversion-Openness Five Factor Inventory, Mastery scale, and Leiden Index of Depression Sensitivity), recent stressful life events (List of Threatening Experiences Questionnaire), and psychosocial variables were administered. The Life Chart Interview was used to determine the time to remission of depressive and/or anxiety disorders. RESULTS: At baseline, 846 participants (57.4%) reported any childhood maltreatment. Childhood maltreatment had a negative impact on psychosocial functioning and was predictive of more unfavorable personality characteristics and cognitive reactivity styles (P < 0.001). Childhood maltreatment was a significant predictor of lower likelihood of remission of depressive and/or anxiety disorders (HR = 0.94, P < 0.001). High levels of neuroticism, hopelessness, external locus of control, and low levels of extraversion were mediating the relationship between childhood maltreatment and 4-year remission of depressive and anxiety disorders. CONCLUSIONS: Certain personality characteristics are key players in the mechanism linking childhood maltreatment to an adverse illness course of depressive and anxiety disorders. Early interventions--reducing neuroticism and hopelessness, and enhancing extraversion and locus of control--might contribute to a better prognosis in a "high-risk" group of depressive and anxiety disorders.

23 Article Six-year longitudinal course and outcomes of subtypes of depression. 2016

Lamers, F / Beekman, A T F / van Hemert, A M / Schoevers, R A / Penninx, B W J H. ·F. Lamers, A. T. F. Beekman, Department of Psychiatry and EMGO Institute for Health and Care Research, VU University Center, Amsterdam; A. M. van Hemert, Department of Psychiatry, Leiden University Medical Center, Leiden; R. A. Schoevers, Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen; B. W. J. H. Penninx, Department of Psychiatry and EMGO Institute for Health and Care Research, VU University Center, Amsterdam, The Netherlands f.lamers@ggzingeest.nl. · F. Lamers, A. T. F. Beekman, Department of Psychiatry and EMGO Institute for Health and Care Research, VU University Center, Amsterdam; A. M. van Hemert, Department of Psychiatry, Leiden University Medical Center, Leiden; R. A. Schoevers, Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen; B. W. J. H. Penninx, Department of Psychiatry and EMGO Institute for Health and Care Research, VU University Center, Amsterdam, The Netherlands. ·Br J Psychiatry · Pubmed #26294366.

ABSTRACT: BACKGROUND: Clinical and aetiological heterogeneity have impeded our understanding of depression. AIMS: To evaluate differences in psychiatric and somatic course between people with depression subtypes that differed clinically (severity) and aetiologically (melancholic v. atypical). METHOD: Data from baseline, 2-, 4- and 6-year follow-up of The Netherlands Study of Depression and Anxiety were used, and included 600 controls and 648 people with major depressive disorder (subtypes: severe melancholic n = 308; severe atypical n = 167; moderate n = 173, established using latent class analysis). RESULTS: Those with the moderate subtype had a significantly better psychiatric clinical course than the severe melancholic and atypical subtype groups. Suicidal thoughts and anxiety persisted longer in those with the melancholic subtype. The atypical subtype group continued to have the highest body mass index and highest prevalence of metabolic syndrome during follow-up, although differences between groups became less pronounced over time. CONCLUSIONS: Course trajectories of depressive subtypes mostly ran parallel to each other, with baseline severity being the most important differentiator in course between groups.

24 Article Economic Evaluation of Concise Cognitive Behavioural Therapy and/or Pharmacotherapy for Depressive and Anxiety Disorders. 2015

Meuldijk, Denise / Carlier, Ingrid V E / van Vliet, Irene M / van Hemert, Albert M / Zitman, Frans G / van den Akker-van Marle, M Elske. ·Leiden University Medical Centre, Department of Psychiatry, P.O. Box 9600, 2300 RC, Leiden, the Netherlands, d.meuldijk@lumc.nl. ·J Ment Health Policy Econ · Pubmed #26729009.

ABSTRACT: BACKGROUND: Depressive and anxiety disorders cause great suffering and disability and are associated with high health care costs. In a previous conducted pragmatic randomised controlled trial, we have shown that a concise format of cognitive behavioural- and/or pharmacotherapy is as effective as standard care in reducing depressive and anxiety symptoms and in improving subdomains of general health and quality of life in secondary care psychiatric outpatients. AIMS OF THE STUDY: In this economic evaluation, we examined whether a favourable cost-utility of concise care compared to standard care was attained. METHODS: The economic evaluation was performed alongside a pragmatic randomised controlled trial. Health-related quality of life was measured using the Short-Form (SF-36) questionnaire. Cost of healthcare utilization and productivity loss (absenteeism and presenteeism) were assessed using the Trimbos/iMTA questionnaire for Costs associated with Psychiatric Illness (TiC-P). A cost-utility analysis, using cost-effectiveness acceptability curves, comparing differences in societal costs and Quality-Adjusted Life Years (QALYs) at 1 year was performed. RESULTS: One year after study entry, the difference in mean cost per patient of the two primary treatments was not significant between both groups. No significant differences in other healthcare and non- healthcare costs could be detected between patients receiving concise care and standard care. Also, QALYs were not statistically different between the groups during the study period. From both the societal and healthcare perspective, the probability that concise care is more cost-effective compared to standard care remains below the turning point of 0.5 for all acceptable values of the willingness to pay for a QALY. The economic evaluation suggests that concise care is unlikely to be cost-effective compared to standard care in the treatment for depressive- and anxiety disorders in secondary mental health care during a one year follow up period. DISCUSSION: Total costs and QALYs were not significantly different between standard and concise care, with no evidence for cost-effectiveness of concise care in the first year. The longer impact of concise care for patients with mild to moderate symptoms of depressive and/or anxiety disorders compared to standard care in secondary care needs to be further studied. IMPLICATIONS: This economic evaluation failed to find significant differences in cost between concise and standard care over the study period of one year. Replication of our economic evaluation might benefit from an extended follow-up period and strict adherence to the study protocol. If concise care will be found to be cost-effective in the long term, this would have major implications for recommendations how to optimize secondary mental health care in the treatment of depressive -- and anxiety disorders.

25 Article Symptom dimensions of affective disorders in migraine patients. 2015

Louter, M A / Pijpers, J A / Wardenaar, K J / van Zwet, E W / van Hemert, A M / Zitman, F G / Ferrari, M D / Penninx, B W / Terwindt, G M. ·Dept. Neurology, Leiden University Medical Centre, Leiden, The Netherlands; Dept. Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands. · Dept. Neurology, Leiden University Medical Centre, Leiden, The Netherlands. · Dept. Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands; University of Groningen, University Medical Centre Groningen, Interdisciplinary Center Psychopathology and Emotion Regulation, Groningen, The Netherlands. · Dept. Biostatistics, Leiden University Medical Centre, Leiden, The Netherlands. · Dept. Psychiatry, Leiden University Medical Centre, Leiden, The Netherlands. · Dept. of Psychiatry, EMGO Institute for Health and Care Research, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands. · Dept. Neurology, Leiden University Medical Centre, Leiden, The Netherlands. Electronic address: g.m.terwindt@lumc.nl. ·J Psychosom Res · Pubmed #26526323.

ABSTRACT: OBJECTIVE: A strong association has been established between migraine and depression. However, this is the first study to differentiate in a large sample of migraine patients for symptom dimensions of the affective disorder spectrum. METHODS: Migraine patients (n=3174) from the LUMINA (Leiden University Medical Centre Migraine Neuro-analysis Program) study and patients with current psychopathology (n=1129), past psychopathology (n=477), and healthy controls (n=561) from the NESDA (Netherlands Study of Depression and Anxiety) study, were compared for three symptom dimensions of depression and anxiety. The dimensions -lack of positive affect (depression specific); negative affect (nonspecific); and somatic arousal (anxiety specific)- were assessed by a shortened adaptation of the Mood and Anxiety Symptom Questionnaire (MASQ-D30). Within the migraine group, the association with migraine specific determinants was established. Multivariate regression analyses were conducted. RESULTS: Migraine patients differed significantly (p<0.001) from healthy controls for all three dimensions: Cohen's d effect sizes were 0.37 for lack of positive affect, 0.68 for negative affect, and 0.75 for somatic arousal. For the lack of positive affect and negative affect dimensions, migraine patients were predominantly similar to the past psychopathology group. For the somatic arousal dimension, migraine patients scores were more comparable with the current psychopathology group. Migraine specific determinants for high scores on all dimensions were high frequency of attacks and cutaneous allodynia during attacks. CONCLUSION: This study shows that affective symptoms in migraine patients are especially associated with the somatic arousal component.

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