10 results
Somatic multicomorbidity and disability in patients with psychiatric disorders in comparison to the general population: a quasi-epidemiological investigation in 54,826 subjects from 40 countries (COMET-G study)
- Konstantinos N. Fountoulakis, Grigorios N. Karakatsoulis, Seri Abraham, Kristina Adorjan, Helal Uddin Ahmed, Renato D. Alarcón, Kiyomi Arai, Sani Salihu Auwal, Michael Berk, Sarah Bjedov, Julio Bobes, Teresa Bobes-Bascaran, Julie Bourgin-Duchesnay, Cristina Ana Bredicean, Laurynas Bukelskis, Akaki Burkadze, Indira Indiana Cabrera Abud, Ruby Castilla-Puentes, Marcelo Cetkovich, Hector Colon-Rivera, Ricardo Corral, Carla Cortez-Vergara, Piirika Crepin, Domenico De Berardis, Sergio Zamora Delgado, David De Lucena, Avinash De Sousa, Ramona Di Stefano, Seetal Dodd, Livia Priyanka Elek, Anna Elissa, Berta Erdelyi-Hamza, Gamze Erzin, Martin J. Etchevers, Peter Falkai, Adriana Farcas, Ilya Fedotov, Viktoriia Filatova, Nikolaos K. Fountoulakis, Iryna Frankova, Francesco Franza, Pedro Frias, Tatiana Galako, Cristian J. Garay, Leticia Garcia-Álvarez, Maria Paz García-Portilla, Xenia Gonda, Tomasz M. Gondek, Daniela Morera González, Hilary Gould, Paolo Grandinetti, Arturo Grau, Violeta Groudeva, Michal Hagin, Takayuki Harada, Tasdik M. Hasan, Nurul Azreen Hashim, Jan Hilbig, Sahadat Hossain, Rossitza Iakimova, Mona Ibrahim, Felicia Iftene, Yulia Ignatenko, Matias Irarrazaval, Zaliha Ismail, Jamila Ismayilova, Asaf Jakobs, Miro Jakovljević, Nenad Jakšić, Afzal Javed, Helin Yilmaz Kafali, Sagar Karia, Olga Kazakova, Doaa Khalifa, Olena Khaustova, Steve Koh, Svetlana Kopishinskaia, Korneliia Kosenko, Sotirios A. Koupidis, Illes Kovacs, Barbara Kulig, Alisha Lalljee, Justine Liewig, Abdul Majid, Evgeniia Malashonkova, Khamelia Malik, Najma Iqbal Malik, Gulay Mammadzada, Bilvesh Mandalia, Donatella Marazziti, Darko Marčinko, Stephanie Martinez, Eimantas Matiekus, Gabriela Mejia, Roha Saeed Memon, Xarah Elenne Meza Martínez, Dalia Mickevičiūtė, Roumen Milev, Muftau Mohammed, Alejandro Molina-López, Petr Morozov, Nuru Suleiman Muhammad, Filip Mustač, Mika S. Naor, Amira Nassieb, Alvydas Navickas, Tarek Okasha, Milena Pandova, Anca-Livia Panfil, Liliya Panteleeva, Ion Papava, Mikaella E. Patsali, Alexey Pavlichenko, Bojana Pejuskovic, Mariana Pinto Da Costa, Mikhail Popkov, Dina Popovic, Nor Jannah Nasution Raduan, Francisca Vargas Ramírez, Elmars Rancans, Salmi Razali, Federico Rebok, Anna Rewekant, Elena Ninoska Reyes Flores, María Teresa Rivera-Encinas, Pilar Saiz, Manuel Sánchez de Carmona, David Saucedo Martínez, Jo Anne Saw, Görkem Saygili, Patricia Schneidereit, Bhumika Shah, Tomohiro Shirasaka, Ketevan Silagadze, Satti Sitanggang, Oleg Skugarevsky, Anna Spikina, Sridevi Sira Mahalingappa, Maria Stoyanova, Anna Szczegielniak, Simona Claudia Tamasan, Giuseppe Tavormina, Maurilio Giuseppe Maria Tavormina, Pavlos N. Theodorakis, Mauricio Tohen, Eva Maria Tsapakis, Dina Tukhvatullina, Irfan Ullah, Ratnaraj Vaidya, Johann M. Vega-Dienstmaier, Jelena Vrublevska, Olivera Vukovic, Olga Vysotska, Natalia Widiasih, Anna Yashikhina, Panagiotis E. Prezerakos, Daria Smirnova
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- CNS Spectrums / Volume 29 / Issue 2 / April 2024
- Published online by Cambridge University Press:
- 25 January 2024, pp. 126-149
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Background
The prevalence of medical illnesses is high among patients with psychiatric disorders. The current study aimed to investigate multi-comorbidity in patients with psychiatric disorders in comparison to the general population. Secondary aims were to investigate factors associated with metabolic syndrome and treatment appropriateness of mental disorders.
MethodsThe sample included 54,826 subjects (64.73% females; 34.15% males; 1.11% nonbinary gender) from 40 countries (COMET-G study). The analysis was based on the registration of previous history that could serve as a fair approximation for the lifetime prevalence of various medical conditions.
ResultsAbout 24.5% reported a history of somatic and 26.14% of mental disorders. Mental disorders were by far the most prevalent group of medical conditions. Comorbidity of any somatic with any mental disorder was reported by 8.21%. One-third to almost two-thirds of somatic patients were also suffering from a mental disorder depending on the severity and multicomorbidity. Bipolar and psychotic patients and to a lesser extent depressives, manifested an earlier (15–20 years) manifestation of somatic multicomorbidity, severe disability, and probably earlier death. The overwhelming majority of patients with mental disorders were not receiving treatment or were being treated in a way that was not recommended. Antipsychotics and antidepressants were not related to the development of metabolic syndrome.
ConclusionsThe finding that one-third to almost two-thirds of somatic patients also suffered from a mental disorder strongly suggests that psychiatry is the field with the most trans-specialty and interdisciplinary value and application points to the importance of teaching psychiatry and mental health in medical schools and also to the need for more technocratically oriented training of psychiatric residents.
Functional neuroimaging biomarkers of anhedonia response to escitalopram plus adjunct aripiprazole treatment for major depressive disorder
- Sophie R. Vaccarino, Shijing Wang, Sakina J. Rizvi, Wendy Lou, Stefanie Hassel, Glenda M. MacQueen, Keith Ho, Benicio N. Frey, Raymond W. Lam, Roumen V. Milev, Susan Rotzinger, Arun V. Ravindran, Stephen C. Strother, Sidney H. Kennedy
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- Journal:
- BJPsych Open / Volume 10 / Issue 1 / January 2024
- Published online by Cambridge University Press:
- 05 January 2024, e18
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Background
Identifying neuroimaging biomarkers of antidepressant response may help guide treatment decisions and advance precision medicine.
AimsTo examine the relationship between anhedonia and functional neurocircuitry in key reward processing brain regions in people with major depressive disorder receiving aripiprazole adjunct therapy with escitalopram.
MethodData were collected as part of the CAN-BIND-1 study. Participants experiencing a current major depressive episode received escitalopram for 8 weeks; escitalopram non-responders received adjunct aripiprazole for an additional 8 weeks. Functional magnetic resonance imaging (on weeks 0 and 8) and clinical assessment of anhedonia (on weeks 0, 8 and 16) were completed. Seed-based correlational analysis was employed to examine the relationship between baseline resting-state functional connectivity (rsFC), using the nucleus accumbens (NAc) and anterior cingulate cortex (ACC) as key regions of interest, and change in anhedonia severity after adjunct aripiprazole.
ResultsAnhedonia severity significantly improved after treatment with adjunct aripiprazole.
There was a positive correlation between anhedonia improvement and rsFC between the ACC and posterior cingulate cortex, ACC and posterior praecuneus, and NAc and posterior praecuneus. There was a negative correlation between anhedonia improvement and rsFC between the ACC and anterior praecuneus and NAc and anterior praecuneus.
ConclusionsEight weeks of aripiprazole, adjunct to escitalopram, was associated with improved anhedonia symptoms. Changes in functional connectivity between key reward regions were associated with anhedonia improvement, suggesting aripiprazole may be an effective treatment for individuals experiencing reward-related deficits. Future studies are required to replicate our findings and explore their generalisability, using other agents with partial dopamine (D2) agonism and/or serotonin (5-HT2A) antagonism.
Pro-inflammatory markers are associated with response to sequential pharmacotherapy in major depressive disorder: a CAN-BIND-1 report
- M. Ishrat Husain, Jane A. Foster, Brittany L. Mason, Sheng Chen, Haoyu Zhao, Wei Wang, Susan Rotzinger, Sakina Rizvi, Keith Ho, Raymond Lam, Glenda MacQueen, Roumen Milev, Benicio N. Frey, Daniel Müller, Gustavo Turecki, Manish Jha, Madhukar Trivedi, Sidney H. Kennedy
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- Journal:
- CNS Spectrums / Volume 28 / Issue 6 / December 2023
- Published online by Cambridge University Press:
- 23 May 2023, pp. 739-746
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Objective
There is limited literature on associations between inflammatory tone and response to sequential pharmacotherapies in major depressive disorder (MDD).
MethodsIn a 16-week open-label clinical trial, 211 participants with MDD were treated with escitalopram 10–20 mg daily for 8 weeks. Responders continued escitalopram while non-responders received adjunctive aripiprazole 2–10 mg daily for 8 weeks. Plasma levels of pro-inflammatory markers—C-reactive protein, interleukin (IL)-1β, IL-6, IL-17, interferon-gamma (IFN)-Γ, tumor necrosis factor (TNF)-α, and Chemokine C–C motif ligand-2 (CCL-2)—measured at baseline, and after 2, 8 and 16 weeks were included in logistic regression analyzes to assess associations between inflammatory markers and treatment response.
ResultsPre-treatment IFN-Γ and CCL-2 levels were significantly associated with a lower of odds of response to escitalopram at 8 weeks. Increases in CCL-2 levels from weeks 8 to 16 in escitalopram non-responders were significantly associated with higher odds of non-response to adjunctive aripiprazole at week 16.
ConclusionHigher pre-treatment levels of IFN-Γ and CCL-2 were associated with non-response to escitalopram. Increasing levels of these pro-inflammatory markers may be associated with non-response to adjunctive aripiprazole. These findings require validation in independent clinical populations.
The concept of “metabolic jet lag” in the pathophysiology of bipolar disorder: implications for research and clinical care
- Elena Koning, Alexandra McDonald, Alexander Bambokian, Fabiano A. Gomes, Jacob Vorstman, Michael Berk, Jennifer Fabe, Roger S. McIntyre, Roumen Milev, Rodrigo B. Mansur, Elisa Brietzke
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- Journal:
- CNS Spectrums / Volume 28 / Issue 5 / October 2023
- Published online by Cambridge University Press:
- 12 December 2022, pp. 571-580
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Bipolar disorder (BD) is a potentially chronic mental disorder marked by recurrent depressive and manic episodes, circadian rhythm disruption, and changes in energetic metabolism. “Metabolic jet lag” refers to a state of shift in circadian patterns of energy homeostasis, affecting neuroendocrine, immune, and adipose tissue function, expressed through behavioral changes such as irregularities in sleep and appetite. Risk factors include genetic variation, mitochondrial dysfunction, lifestyle factors, poor gut microbiome health and abnormalities in hunger, satiety, and hedonistic function. Evidence suggests metabolic jet lag is a core component of BD pathophysiology, as individuals with BD frequently exhibit irregular eating rhythms and circadian desynchronization of their energetic metabolism, which is associated with unfavorable clinical outcomes. Although current diagnostic criteria lack any assessment of eating rhythms, technological advancements including mobile phone applications and ecological momentary assessment allow for the reliable tracking of biological rhythms. Overall, methodological refinement of metabolic jet lag assessment will increase knowledge in this field and stimulate the development of interventions targeting metabolic rhythms, such as time-restricted eating.
Prediction of depression treatment outcome from multimodal data: a CAN-BIND-1 report
- Mehri Sajjadian, Rudolf Uher, Keith Ho, Stefanie Hassel, Roumen Milev, Benicio N. Frey, Faranak Farzan, Pierre Blier, Jane A. Foster, Sagar V. Parikh, Daniel J. Müller, Susan Rotzinger, Claudio N. Soares, Gustavo Turecki, Valerie H. Taylor, Raymond W. Lam, Stephen C. Strother, Sidney H. Kennedy
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- Journal:
- Psychological Medicine / Volume 53 / Issue 12 / September 2023
- Published online by Cambridge University Press:
- 25 August 2022, pp. 5374-5384
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Background
Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers.
MethodsIn the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively.
ResultsA combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction.
ConclusionsA combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.
Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis
- Mehri Sajjadian, Raymond W. Lam, Roumen Milev, Susan Rotzinger, Benicio N. Frey, Claudio N. Soares, Sagar V. Parikh, Jane A. Foster, Gustavo Turecki, Daniel J. Müller, Stephen C. Strother, Faranak Farzan, Sidney H. Kennedy, Rudolf Uher
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- Psychological Medicine / Volume 51 / Issue 16 / December 2021
- Published online by Cambridge University Press:
- 12 October 2021, pp. 2742-2751
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Background
Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.
MethodsOf 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.
ResultsEight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56–0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72–0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.
ConclusionsThe negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.
Gender, age at onset, and duration of being ill as predictors for the long-term course and outcome of schizophrenia: an international multicenter study
- Konstantinos N. Fountoulakis, Elena Dragioti, Antonis T. Theofilidis, Tobias Wiklund, Xenofon Atmatzidis, Ioannis Nimatoudis, Erik Thys, Martien Wampers, Luchezar Hranov, Trayana Hristova, Daniil Aptalidis, Roumen Milev, Felicia Iftene, Filip Spaniel, Pavel Knytl, Petra Furstova, Tiina From, Henry Karlsson, Maija Walta, Raimo K. R. Salokangas, Jean-Michel Azorin, Justine Bouniard, Julie Montant, Georg Juckel, Ida S. Haussleiter, Athanasios Douzenis, Ioannis Michopoulos, Panagiotis Ferentinos, Nikolaos Smyrnis, Leonidas Mantonakis, Zsófia Nemes, Xenia Gonda, Dora Vajda, Anita Juhasz, Amresh Shrivastava, John Waddington, Maurizio Pompili, Anna Comparelli, Valentina Corigliano, Elmars Rancans, Alvydas Navickas, Jan Hilbig, Laurynas Bukelskis, Lidija I. Stevovic, Sanja Vodopic, Oluyomi Esan, Oluremi Oladele, Christopher Osunbote, Janusz K. Rybakowski, Pawel Wojciak, Klaudia Domowicz, Maria L. Figueira, Ludgero Linhares, Joana Crawford, Anca-Livia Panfil, Daria Smirnova, Olga Izmailova, Dusica Lecic-Tosevski, Henk Temmingh, Fleur Howells, Julio Bobes, Maria P. Garcia-Portilla, Leticia García-Alvarez, Gamze Erzin, Hasan Karadağ, Avinash De Sousa, Anuja Bendre, Cyril Hoschl, Cristina Bredicean, Ion Papava, Olivera Vukovic, Bojana Pejuskovic, Vincent Russell, Loukas Athanasiadis, Anastasia Konsta, Nikolaos K. Fountoulakis, Dan Stein, Michael Berk, Olivia Dean, Rajiv Tandon, Siegfried Kasper, Marc De Hert
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- CNS Spectrums / Volume 27 / Issue 6 / December 2022
- Published online by Cambridge University Press:
- 09 August 2021, pp. 716-723
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Background
The aim of the current study was to explore the effect of gender, age at onset, and duration on the long-term course of schizophrenia.
MethodsTwenty-nine centers from 25 countries representing all continents participated in the study that included 2358 patients aged 37.21 ± 11.87 years with a DSM-IV or DSM-5 diagnosis of schizophrenia; the Positive and Negative Syndrome Scale as well as relevant clinicodemographic data were gathered. Analysis of variance and analysis of covariance were used, and the methodology corrected for the presence of potentially confounding effects.
ResultsThere was a 3-year later age at onset for females (P < .001) and lower rates of negative symptoms (P < .01) and higher depression/anxiety measures (P < .05) at some stages. The age at onset manifested a distribution with a single peak for both genders with a tendency of patients with younger onset having slower advancement through illness stages (P = .001). No significant effects were found concerning duration of illness.
DiscussionOur results confirmed a later onset and a possibly more benign course and outcome in females. Age at onset manifested a single peak in both genders, and surprisingly, earlier onset was related to a slower progression of the illness. No effect of duration has been detected. These results are partially in accord with the literature, but they also differ as a consequence of the different starting point of our methodology (a novel staging model), which in our opinion precluded the impact of confounding effects. Future research should focus on the therapeutic policy and implications of these results in more representative samples.
Modeling psychological function in patients with schizophrenia with the PANSS: an international multi-center study
- Konstantinos N. Fountoulakis, Elena Dragioti, Antonis T. Theofilidis, Tobias Wiklund, Xenofon Atmatzidis, Ioannis Nimatoudis, Erik Thys, Martien Wampers, Luchezar Hranov, Trayana Hristova, Daniil Aptalidis, Roumen Milev, Felicia Iftene, Filip Spaniel, Pavel Knytl, Petra Furstova, Tiina From, Henry Karlsson, Maija Walta, Raimo K.R. Salokangas, Jean-Michel Azorin, Justine Bouniard, Julie Montant, Georg Juckel, Ida S. Haussleiter, Athanasios Douzenis, Ioannis Michopoulos, Panagiotis Ferentinos, Nikolaos Smyrnis, Leonidas Mantonakis, Zsófia Nemes, Xenia Gonda, Dora Vajda, Anita Juhasz, Amresh Shrivastava, John Waddington, Maurizio Pompili, Anna Comparelli, Valentina Corigliano, Elmars Rancans, Alvydas Navickas, Jan Hilbig, Laurynas Bukelskis, Lidija I. Stevovic, Sanja Vodopic, Oluyomi Esan, Oluremi Oladele, Christopher Osunbote, Janusz K. Rybakowski, Pawel Wojciak, Klaudia Domowicz, Maria L. Figueira, Ludgero Linhares, Joana Crawford, Anca-Livia Panfil, Daria Smirnova, Olga Izmailova, Dusica Lecic-Tosevski, Henk Temmingh, Fleur Howells, Julio Bobes, Maria P. Garcia-Portilla, Leticia García-Alvarez, Gamze Erzin, Hasan Karadağ, Avinash De Sousa, Anuja Bendre, Cyril Hoschl, Cristina Bredicean, Ion Papava, Olivera Vukovic, Bojana Pejuskovic, Vincent Russell, Loukas Athanasiadis, Anastasia Konsta, Dan Stein, Michael Berk, Olivia Dean, Rajiv Tandon, Siegfried Kasper, Marc De Hert
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- Journal:
- CNS Spectrums / Volume 26 / Issue 3 / June 2021
- Published online by Cambridge University Press:
- 15 April 2020, pp. 290-298
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Background
The aim of the current study was to explore the changing interrelationships among clinical variables through the stages of schizophrenia in order to assemble a comprehensive and meaningful disease model.
MethodsTwenty-nine centers from 25 countries participated and included 2358 patients aged 37.21 ± 11.87 years with schizophrenia. Multiple linear regression analysis and visual inspection of plots were performed.
ResultsThe results suggest that with progression stages, there are changing correlations among Positive and Negative Syndrome Scale factors at each stage and each factor correlates with all the others in that particular stage, in which this factor is dominant. This internal structure further supports the validity of an already proposed four stages model, with positive symptoms dominating the first stage, excitement/hostility the second, depression the third, and neurocognitive decline the last stage.
ConclusionsThe current study investigated the mental organization and functioning in patients with schizophrenia in relation to different stages of illness progression. It revealed two distinct “cores” of schizophrenia, the “Positive” and the “Negative,” while neurocognitive decline escalates during the later stages. Future research should focus on the therapeutic implications of such a model. Stopping the progress of the illness could demand to stop the succession of stages. This could be achieved not only by both halting the triggering effect of positive and negative symptoms, but also by stopping the sensitization effect on the neural pathways responsible for the development of hostility, excitement, anxiety, and depression as well as the deleterious effect on neural networks responsible for neurocognition.
Childhood maltreatment and cognitive functioning in patients with major depressive disorder: a CAN-BIND-1 report
- Trisha Chakrabarty, Kate L. Harkness, Shane J. McInerney, Lena C. Quilty, Roumen V. Milev, Sidney H. Kennedy, Benicio N. Frey, Glenda M. MacQueen, Daniel J. Müller, Susan Rotzinger, Rudolf Uher, Raymond W. Lam
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- Journal:
- Psychological Medicine / Volume 50 / Issue 15 / November 2020
- Published online by Cambridge University Press:
- 04 October 2019, pp. 2536-2547
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Background
Patients with major depressive disorder (MDD) display cognitive deficits in acutely depressed and remitted states. Childhood maltreatment is associated with cognitive dysfunction in adults, but its impact on cognition and treatment related cognitive outcomes in adult MDD has received little consideration. We investigate whether, compared to patients without maltreatment and healthy participants, adult MDD patients with childhood maltreatment display greater cognitive deficits in acute depression, lower treatment-associated cognitive improvements, and lower cognitive performance in remission.
MethodsHealthy and acutely depressed MDD participants were enrolled in a multi-center MDD predictive marker discovery trial. MDD participants received 16 weeks of standardized antidepressant treatment. Maltreatment and cognition were assessed with the Childhood Experience of Care and Abuse interview and the CNS Vital Signs battery, respectively. Cognitive scores and change from baseline to week 16 were compared amongst MDD participants with (DM+, n = 93) and without maltreatment (DM−, n = 90), and healthy participants with (HM+, n = 22) and without maltreatment (HM−, n = 80). Separate analyses in MDD participants who remitted were conducted.
ResultsDM+ had lower baseline global cognition, processing speed, and memory v. HM−, with no significant baseline differences amongst DM−, HM+, and HM− groups. There were no significant between-group differences in cognitive change over 16 weeks. Post-treatment remitted DM+, but not remitted DM−, scored significantly lower than HM− in working memory and processing speed.
ConclusionsChildhood maltreatment was associated with cognitive deficits in depressed and remitted adults with MDD. Maltreatment may be a risk factor for more severe and persistent cognitive deficits in adult MDD.
Early change in reward and punishment sensitivity as a predictor of response to antidepressant treatment for major depressive disorder: a CAN-BIND-1 report
- Timothy A. Allen, Raymond W. Lam, Roumen Milev, Sakina J. Rizvi, Benicio N. Frey, Glenda M. MacQueen, Daniel J. Müller, Rudolf Uher, Sidney H. Kennedy, Lena C. Quilty
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- Psychological Medicine / Volume 49 / Issue 10 / July 2019
- Published online by Cambridge University Press:
- 17 September 2018, pp. 1629-1638
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Background
In an effort to optimize patient outcomes, considerable attention is being devoted to identifying patient characteristics associated with major depressive disorder (MDD) and its responsiveness to treatment. In the current study, we extend this work by evaluating whether early change in these sensitivities is associated with response to antidepressant treatment for MDD.
MethodsParticipants included 210 patients with MDD who were treated with 8 weeks of escitalopram and 112 healthy comparison participants. Of the original 210 patients, 90 non-responders received adjunctive aripiprazole for an additional 8 weeks. Symptoms of depression and anhedonia were assessed at the beginning of treatment and 8 weeks later in both samples. Reward and punishment sensitivity were assessed using the BIS/BAS scales measured at the initiation of treatment and 2 weeks later.
ResultsIndividuals with MDD exhibited higher punishment sensitivity and lower reward sensitivity compared with healthy comparison participants. Change in reward sensitivity during the first 2 weeks of treatment was associated with improved depressive symptoms and anhedonia following 8 weeks of treatment with escitalopram. Similarly, improvement in reward responsiveness during the first 2 weeks of adjunctive therapy with aripiprazole was associated with fewer symptoms of depression at post-treatment.
ConclusionsFindings highlight the predictive utility of early change in reward sensitivity during antidepressant treatment for major depression. In a clinical setting, a lack of change in early reward processing may signal a need to modify a patient's treatment plan with alternative or augmented treatment approaches.