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The search for relevant biomarkers of major depressive disorder (MDD) is challenged by heterogeneity; biological alterations may vary in patients expressing different symptom profiles. Moreover, most research considers a limited number of biomarkers, which may not be adequate for tagging complex network-level mechanisms. Here we studied clusters of proteins and examined their relation with MDD and individual depressive symptoms.
The sample consisted of 1621 subjects from the Netherlands Study of Depression and Anxiety (NESDA). MDD diagnoses were based on DSM-IV criteria and the Inventory of Depressive Symptomatology questionnaire measured endorsement of 30 symptoms. Serum protein levels were detected using a multi-analyte platform (171 analytes, immunoassay, Myriad RBM DiscoveryMAP 250+). Proteomic clusters were computed using weighted correlation network analysis (WGCNA).
Six proteomic clusters were identified, of which one was nominally significantly associated with current MDD (p = 9.62E-03, Bonferroni adj. p = 0.057). This cluster contained 21 analytes and was enriched with pathways involved in inflammation and metabolism [including C-reactive protein (CRP), leptin and insulin]. At the individual symptom level, this proteomic cluster was associated with ten symptoms, among which were five atypical, energy-related symptoms. After correcting for several health and lifestyle covariates, hypersomnia, increased appetite, panic and weight gain remained significantly associated with the cluster.
Our findings support the idea that alterations in a network of proteins involved in inflammatory and metabolic processes are present in MDD, but these alterations map predominantly to clinical symptoms reflecting an imbalance between energy intake and expenditure.
The treatment of depression in patients with somatic disorders is crucial, given its negative impact on quality of life (QoL), functioning, and even on the somatic disease prognosis. We aimed to examine the most updated evidence on the effects of psychotherapy in patients with depression and somatic disorders, including HIV, oncological, cardiometabolic, and neurological disorders.
We conducted a meta-analysis of 75 randomized trials (8209 participants) of psychotherapy for adults with somatic disorders and a diagnosis or elevated symptoms of depression. Outcomes included depression, QoL, somatic health-related outcomes, and mortality.
Psychotherapy significantly reduced the severity of depression at post-treatment across all categories of somatic disorders (Hedges'g = 0.65; 95% CI 0.52–0.79), with sustained effects at 6–11 months (g = 0.38; 95% CI 0.22–0.53) and at 12 months follow-up or longer (g = 0.13; 95% CI 0.04–0.21). Psychotherapy also showed significant effects on QoL (g = 0.26; 95% CI 0.17–0.35), maintained up to 11 months follow-up (g = 0.25; 95% CI 0.16–0.34). No significant effects were observed on the most frequently reported somatic health-related outcomes (glycemic control, pain), and neither on mortality. Heterogeneity in most analyses was very high, and only 29 (38%) trials were rated at low risk of bias (RoB).
Psychotherapy may be an effective treatment option for patients with depression and somatic disorders, with long-term effects on depression severity and QoL. However, these results should be interpreted with caution due to heterogeneity and RoB.
Disease trajectories of patients with anxiety disorders are highly diverse and approximately 60% remain chronically ill. The ability to predict disease course in individual patients would enable personalized management of these patients. This study aimed to predict recovery from anxiety disorders within 2 years applying a machine learning approach.
In total, 887 patients with anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia, or social phobia) were selected from a naturalistic cohort study. A wide array of baseline predictors (N = 569) from five domains (clinical, psychological, sociodemographic, biological, lifestyle) were used to predict recovery from anxiety disorders and recovery from all common mental disorders (CMDs: anxiety disorders, major depressive disorder, dysthymia, or alcohol dependency) at 2-year follow-up using random forest classifiers (RFCs).
At follow-up, 484 patients (54.6%) had recovered from anxiety disorders. RFCs achieved a cross-validated area-under-the-receiving-operator-characteristic-curve (AUC) of 0.67 when using the combination of all predictor domains (sensitivity: 62.0%, specificity 62.8%) for predicting recovery from anxiety disorders. Classification of recovery from CMDs yielded an AUC of 0.70 (sensitivity: 64.6%, specificity: 62.3%) when using all domains. In both cases, the clinical domain alone provided comparable performances. Feature analysis showed that prediction of recovery from anxiety disorders was primarily driven by anxiety features, whereas recovery from CMDs was primarily driven by depression features.
The current study showed moderate performance in predicting recovery from anxiety disorders over a 2-year follow-up for individual patients and indicates that anxiety features are most indicative for anxiety improvement and depression features for improvement in general.
The main objective of “Lifebrain” is to identify the determinants of brain, cognitive and mental (BCM) health at different stages of life. By integrating, harmonising and enriching major European neuroimaging studies across the life span, we will merge fine-grained BCM health measures of more than 5000 individuals. Longitudinal brain imaging, genetic and health data are available for a major part, as well as cognitive and mental health measures for the broader cohorts, exceeding 27,000 examinations in total. By linking these data to other databases and biobanks, including birth registries, national and regional archives, and by enriching them with a new online data collection and novel measures, we will address the risk factors and protective factors of BCM health. We will identify pathways through which risk and protective factors work and their moderators. Exploiting existing European infrastructures and initiatives, we hope to make major conceptual, methodological and analytical contributions towards large integrative cohorts and their efficient exploitation. We will thus provide novel information on BCM health maintenance, as well as the onset and course of BCM disorders. This will lay a foundation for earlier diagnosis of brain disorders, aberrant development and decline of BCM health, and translate into future preventive and therapeutic strategies. Aiming to improve clinical practice and public health we will work with stakeholders and health authorities, and thus provide the evidence base for prevention and intervention.
Childhood maltreatment (CM) plays an important role in the development of major depressive disorder (MDD). The aim of this study was to examine whether CM severity and type are associated with MDD-related brain alterations, and how they interact with sex and age.
Within the ENIGMA-MDD network, severity and subtypes of CM using the Childhood Trauma Questionnaire were assessed and structural magnetic resonance imaging data from patients with MDD and healthy controls were analyzed in a mega-analysis comprising a total of 3872 participants aged between 13 and 89 years. Cortical thickness and surface area were extracted at each site using FreeSurfer.
CM severity was associated with reduced cortical thickness in the banks of the superior temporal sulcus and supramarginal gyrus as well as with reduced surface area of the middle temporal lobe. Participants reporting both childhood neglect and abuse had a lower cortical thickness in the inferior parietal lobe, middle temporal lobe, and precuneus compared to participants not exposed to CM. In males only, regardless of diagnosis, CM severity was associated with higher cortical thickness of the rostral anterior cingulate cortex. Finally, a significant interaction between CM and age in predicting thickness was seen across several prefrontal, temporal, and temporo-parietal regions.
Severity and type of CM may impact cortical thickness and surface area. Importantly, CM may influence age-dependent brain maturation, particularly in regions related to the default mode network, perception, and theory of mind.
Etiological research of depression and anxiety disorders has been hampered by diagnostic heterogeneity. In order to address this, researchers have tried to identify more homogeneous patient subgroups. This work has predominantly focused on explaining interpersonal heterogeneity based on clinical features (i.e. symptom profiles). However, to explain interpersonal variations in underlying pathophysiological mechanisms, it might be more effective to take biological heterogeneity as the point of departure when trying to identify subgroups. Therefore, this study aimed to identify data-driven subgroups of patients based on biomarker profiles.
Data of patients with a current depressive and/or anxiety disorder came from the Netherlands Study of Depression and Anxiety, a large, multi-site naturalistic cohort study (n = 1460). Thirty-six biomarkers (e.g. leptin, brain-derived neurotrophic factor, tryptophan) were measured, as well as sociodemographic and clinical characteristics. Latent class analysis of the discretized (lower 10%, middle, upper 10%) biomarkers were used to identify different patient clusters.
The analyses resulted in three classes, which were primarily characterized by different levels of metabolic health: ‘lean’ (21.6%), ‘average’ (62.2%) and ‘overweight’ (16.2%). Inspection of the classes’ clinical features showed the highest levels of psychopathology, severity and medication use in the overweight class.
The identified classes were strongly tied to general (metabolic) health, and did not reflect any natural cutoffs along the lines of the traditional diagnostic classifications. Our analyses suggested that especially poor metabolic health could be seen as a distal marker for depression and anxiety, suggesting a relationship between the ‘overweight’ subtype and internalizing psychopathology.
Seasonal affective disorder (SAD) is considered to be a subtype of depression.
To compare the clinical picture of SAD to non-seasonal affective disorders (non-SADs).
Diagnoses according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) were established in 2185 participants of the Netherlands Study of Depression and Anxiety. The Seasonal Pattern Assessment Questionnaire was administered to diagnose SAD. Symptoms of depression and anxiety were measured with the Inventory of Depressive Symptoms, the Beck Anxiety Inventory and the Fear Questionnaire.
Participants with SAD, participants with a lifetime bipolar disorder and participants with a lifetime comorbid anxiety and depressive disorder scored highest in terms of psychopathology in the past year. The seasonal distribution of major depressive episodes was not different for participants with or without SAD.
SAD may be a measure of severity of depression with a subjectively perceived worsening of symptoms in the winter months.
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