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The network theory conceptualizes mental disorders as complex networks of symptoms influencing each other by creating feedback loops, leading to a self-sustained syndromic constellation. Symptoms central to the network have the greatest impact in sustaining the rest of symptoms. This analysis focused on the network structure of depressive symptoms in late-life because of their distinct etiologic factors, clinical presentation, and outcomes. We analyzed cross-sectional data from wave 2 of the 19 country Survey of Health, Ageing, and Retirement in Europe (SHARE) and included non-institutionalized adults aged 65 years or older (mean age 74 years, 59% females) endorsing at least one depressive symptom on the EURO-D scale for depression (N =8,557). We characterized the network structure of depressive symptoms in late-life and used indices of “strength”, “betweenness”, and “closeness” to identify symptoms central to the network. We used a case-dropping bootstrap procedure to assess network stability. Death wishes, depressed mood, loss of interest, and pessimism had the highest values of centrality. Insomnia, fatigue and appetite changes had lower centrality values. The identified network remained stable after dropping 74.5% of the sample. Sex or age did not significantly influence the network structure. In conclusion, death wishes, depressed mood, loss of interest, and pessimism constitute the “backbone” that sustains depressive symptoms in late-life. Symptoms central to the network of depressive symptoms may be used as targets for novel, focused interventions and in studies investigating neurobiological processes central to late-life depression.

Abstract The concept of insight is used to indicate the propensity of patients with schizophrenia and other severe mental disorders to recognize their illness and engage in treatment. Thus, insight may have notable consequences for the ill individual: Those who lack insight are at higher risk of nonadherence to treatments, negative clinical outcomes, and worse community functioning. Although insight is an intuitive concept, its essence remains difficult to capture. However, many rating scales are available to aid assessment, both for clinical and research purposes. Insight cannot be reduced to a symptom, a psychological mechanism, or a neuropsychological function. It is likely to have dynamic relationships with all these dimensions and with responses to personal events and contextual factors. In particular, social consequences of mental illness and explanatory models that are alternative to the medical model may fundamentally shape insight and treatment choice. Moreover, the cultural or individual stigmatization of mental illness may turn the acquisition of insight into a painful event and increase the risk of depression. Clinicians need to carefully evaluate and promote insight through a personalized approach to aid patient process of care and personal growth.

Major depression is associated with premature mortality, largely explained by heightened cardiovascular burden. This narrative review summarizes secondary literature (i.e., reviews and meta-analyses) on this topic, considering physical exercise as a potential tool to counteract this alarming phenomenon. Compared to healthy controls, individuals with depression consistently present heightened cardiovascular risk, including “classical” risk factors and dysregulation of pertinent homeostatic systems (immune system, hypothalamic–pituitary–adrenal axis and autonomic nervous system). Ultimately, both genetic background and behavioral abnormalities contribute to explain the link between depression and cardiovascular mortality. Physical inactivity is particularly common in depressed populations and may represent an elective therapeutic target to address premature mortality. Exercise-based interventions, in fact, have proven effective reducing cardiovascular risk and mortality through different mechanisms, although evidence still needs to be replicated in depressed populations. Notably, exercise also directly improves depressive symptoms. Despite its potential, however, exercise remains under-prescribed to depressed individuals. Public health may be the ideal setting to develop and disseminate initiatives that promote the prescription and delivery of exercise-based interventions, with a particular focus on their cost-effectiveness.

Background Demoralisation is a clinically significant problem among cancer patients with a prevalence of 13%–18%. It is defined by difficulty in adjusting to a stressor, wherein the person feels trapped in their predicament and experiences helplessness, hopelessness, loss of confidence and loss of meaning in life. Demoralisation has a strong link with the desire for hastened death and suicidal ideation among the medically ill. This study explored whether a group of symptoms could be identified, distinct from depression, but consistent with adjustment difficulties with demoralisation and linked to ideation of death and suicide. Methods Exploratory Graph Analysis, a network psychometrics technique, was conducted on a large German study of 1529 cancer patients. Demoralisation was measured with the Demoralisation Scale II and depressive symptoms with the PHQ‐9. Results A network of symptoms, with four stable communities, was identified: 1. Loss of hope and meaning; 2. Non‐specific emotionality; 3. Entrapment; 4. Depressive symptoms. The first three communities were clearly distinct from the PHQ‐9 depressive symptoms, except for suicidality and fear of failure. Community 1, Loss of hope and meaning, had the strongest association with thoughts of death and suicide. Hopelessness, loss of role in life, tiredness, pointlessness and feeling trapped were the most central symptoms in the network. Conclusions Communities 1 to 3 are consistent with poor coping without anhedonia and other classic depression symptoms, but linked to suicidal ideation. For people facing the existential threat of cancer, this may indicate poor psychological adjustment to the stressors of their illness. Demoralisation is a prevalent and clinically significant problem among cancer patients, more highly associated with suicidal thinking than depression. New psychometric techniques using network analysis provide evidence that demoralisation may be an important element of psychological adjustment, explaining the independent link of adjustment disorder to suicidality and potentially contributing to a more clinically useful conceptualisation of adjustment disorder.

The longitudinal course of late-life depression remains under-studied. To describe transitions along the depression continuum in old age and to identify factors associated with specific transition patterns. We analysed 15-year longitudinal data on 2745 dementia-free persons aged 60+ from the population-based Swedish National Study on Aging and Care in Kungsholmen. Depression (minor and major) was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision; subsyndromal depression (SSD) was operationalised as the presence of ≥2 symptoms without depression. Multistate survival models were used to map depression transitions, including death, and to examine the association of psychosocial (social network, connection and support), lifestyle (smoking, alcohol consumption and physical activity) and clinical (somatic disease count) factors with transition patterns. Over the follow-up, 19.1% had ≥1 transitions across depressive states, while 6.5% had ≥2. Each additional somatic disease was associated with a higher hazard of progression from no depression (No Dep) to SSD (hazard ratio 1.09; 1.07-1.10) and depression (Dep) (hazard ratio 1.06; 1.04-1.08), but also with a lower recovery (HR 0.95; 0.93-0.97 [where 'HR' refers to 'hazard ratio']; HR 0.96; 0.93-0.99). Physical activity was associated with an increased hazard of recovery to no depression from SSD (hazard ratio 1.49; 1.28-1.73) and depression (hazard ratio 1.20; 1.00-1.44), while a richer social network was associated with both higher recovery from (HR 1.44; 1.26-1.66; HR 1.51; 1.34-1.71) and lower progression hazards to a worse depressive state (HR 0.81; 0.70-0.94; HR 0.58; 0.46-0.73; HR 0.66; 0.44-0.98). Older people may present with heterogeneous depressive trajectories. Targeting the accumulation of somatic diseases and enhancing social interactions may be appropriate for both depression prevention and burden reduction, while promoting physical activity may primarily benefit recovery from depressive disorders.

The clinical presentation of late-life depression is highly heterogeneous and likely influenced by the co-presence of somatic diseases. Using a network approach, this study aims to explore how depressive symptoms are interconnected with each other, as well as with different measures of somatic disease burden in older adults. We examined cross-sectional data on 2860 individuals aged 60+ from the Swedish National Study on Aging and Care in Kungsholmen, Stockholm. The severity of sixteen depressive symptoms was clinically assessed with the Comprehensive Psychopathological Rating Scale. We combined data from individual clinical assessment and health-registers to construct eight system-specific disease clusters (cardiovascular, neurological, gastrointestinal, metabolic, musculoskeletal, respiratory, sensory, and unclassified), along with a measure of overall somatic burden. The interconnection among depressive symptoms, and with disease clusters was explored through networks based on Spearman partial correlations. Bridge centrality index and network loadings were employed to identify depressive symptoms directly connecting disease clusters and depression. Sadness, pessimism, anxiety, and suicidal thoughts were the most interconnected symptoms of the depression network, while somatic symptoms of depression were less interconnected. In the network integrating depressive symptoms with disease clusters, suicidal thoughts, reduced appetite, and cognitive difficulties constituted the most consistent bridge connections. The same bridge symptoms emerged when considering an overall measure of somatic disease burden. Suicidal thoughts, reduced appetite, and cognitive difficulties may play a key role in the interconnection between late-life depression and somatic diseases. If confirmed in longitudinal studies, these bridging symptoms could constitute potential targets in the prevention of late-life depression.

Background Neuroleptic malignant syndrome (NMS) is a rare, severe, idiosyncratic adverse reaction to antipsychotics. Second-generation antipsychotics (SGAs) were originally assumed to be free from the risk of causing NMS, however several cases of NMS induced by SGAs (SGA-NMS) have been reported. Objectives The aim of this study was to systematically review available studies and case reports on SGA-NMS and compare the presentation of NMS induced by different SGAs. Data Sources Citations were retrieved from PubMed up to November 2013, and from reference lists of relevant citations. Study Eligibility Criteria Eligibility criteria included (a) primary studies reporting data on NMS, with at least 50 % of the sample receiving SGAs; or (b) case reports and case reviews reporting on NMS induced by SGA monotherapy, excluding those due to antipsychotic withdrawal. Study Appraisal and Synthesis Methods A standardized method for data extraction and coding was developed for the analysis of eligible case reports. Results Six primary studies and 186 individual cases of NMS induced by SGAs were included. Primary studies suggest that SGA-NMS is characterized by lower incidence, lower clinical severity, and less frequent lethal outcome than NMS induced by first-generation antipsychotics. Systematic analysis of case reports suggests that even the most recently marketed antipsychotics are not free from the risk of inducing NMS. Furthermore, clozapine-, aripiprazole- and amisulpride-induced NMS can present with atypical features more frequently than other SGA-NMS, i.e. displaying less intense extrapyramidal symptoms or high fever. Limitations Case reports report non-systematic data, therefore analyses may be subject to bias. Conclusions and Implications of Key Findings Clinicians should be aware that NMS is virtually associated with all antipsychotics, including those most recently marketed. Although apparently less severe than NMS induced by older antipsychotics, SGA-NMS still represent a relevant clinical issue.

Late-life depression has multiple, heterogeneous clinical presentations. The aim of the study was to identify higher-order homogeneous clinical features (symptom complexes), while accounting for their potential causal interactions within the network approach to psychopathology. We analyzed cross-sectional data from community-dwelling adults aged 65–85 years recruited by the European MentDis_ICF65+ study (n = 2623, mean age 74, 49% females). The severity of 33 depressive symptoms was derived from the age-adapted Composite International Diagnostic Interview. Symptom complexes were identified using multiple detection algorithms for symptom networks, and their fit to data was assessed with latent network models (LNMs) in exploratory and confirmatory analyses. Sensitivity analyses included the Partial Correlation Likelihood Test (PCLT) to investigate the data-generating structure. Depressive symptoms were organized by the Walktrap algorithm into eight symptom complexes, namely sadness/hopelessness, anhedonia/lack of energy, anxiety/irritability, self-reproach, disturbed sleep, agitation/increased appetite, concentration/decision making, and thoughts of death. An LNM adequately fit the distribution of individual symptoms’ data in the population. The model suggested the presence of reciprocal interactions between the symptom complexes of sadness and anxiety, concentration and self-reproach and between self-reproach and thoughts of death. Results of the PCLT confirmed that symptom complex data were more likely generated by a network, rather than a latent-variable structure. In conclusion, late-life depressive symptoms are organized into eight interacting symptom complexes. Identification of the symptom complexes of late-life depression may streamline clinical assessment, provide targets for personalization of treatment, and aid the search for biomarkers and for predictors of outcomes of late-life depression.

Background Depression in people with cancer is common and debilitating, but few instruments exist for early identification. We aimed at developing streamlined Risk Prediction Models (Arturo RPMs) for identifying individuals at higher risk for depression. Methods Predictors of depression were identified from a review of available literature. Then, we used data from the Survey of Health, Ageing and Retirement in Europe (SHARE) prospective study. SHARE recruited community residing adults aged 55 years or older, from which we selected those who reported having received a diagnosis of cancer. The outcome was the presence of depression (EURO-D score ≥ 4) in the Classification Approach (CA), and the severity of depression at a two-year follow up evaluation (EURO-D sum-score) in the Regression Approach (RA). Multiple RPMs were developed using combinations of sample balancing techniques (no balancing, under- or oversampling), learning methods (Generalized Linear Models (GLM), Decision Trees (DT), Random Forest (RF)) and variable selection methods (none, Backward (BW) and Forward (FW) sequential, Genetic Algorithm (GA)). Results We identified 90 predictors of depression that were measured in the SHARE dataset and, combining waves 4 to 8, selected a sample of 4057 participants with cancer. Of these, 33.5% were depressed at 2 years follow-up. In the classification approach the RPM based on undersampling, GLM and GA variable selection used 34 predictors and reached satisfying accuracy (74.4%, AUC-ROC: 0.80; PPV: 84.7%; NPV: 60.1%). In the regression approach, the GLM model with Genetic Algorithm reached the best accuracy (75.1%, AUC-ROC: 0.81; PPV: 70.5%; NPV: 76.2%). The calibration curve suggested a satisfactory level of prediction, homogeneous at all levels of risk thresholds. Using a threshold of 50% risk, the model yields a PPV of 80% and an NPV of 75%. Conclusions The Arturo RPMs can identify older adults with cancer who are at higher risk of developing depression over the following two years. The model is freely available as a web-based calculator for use by individuals, clinicians, and policy makers. Arturo might help to target preventive interventions. Clinical trial number Not applicable.