Efficiency of psychological interventions in the prevention of suicidal behavior and self-injury in penitentiary population: A systematic review.

INTRODUCTION: Suicidal behavior is an important public health problem, with a high prevalence in penitentiary context. Nowadays, there is a wide variety of specific treatment programs, aimed to prevent suicidal and self-injurious behavior in incarcerated people. These programs show relative efficiency depending on the model of the psychological intervention applied. This systematic review evaluates the efficiency of suicidal and self-injurious behavior prevention programs in prisons. METHOD: Empirical studies, evaluating prevention programs for suicidal and self-injurious behavior in penitentiary context were considered for inclusion. PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) directives were followed. Studies from 1990 to 2022 were reviewed, based on the review developed by Winicov (2019) that covered the time lapse between 1990 and 2015. Articles from 2015 to 2022 were located by database research (EBSCOHost, ScienceDirect, PubMed & ProQuest). RESULTS: 44,050 potential studies were identified. Eighteen were included in this systematic review (9 studies by Winicov, 2019). 14 studies showed efficacy of intervention programs on self-injury behavior. The use of Cognitive Behavioral Therapy (CBT) reduced suicidal ideation. In addition, positive results were observed in 3 studies using third-generation therapies as an intervention. CONCLUSIONS: Suicidal and self-injurious behavior in prison shows lower levels of incidence when specific treatment programs are applied. It's crucial to increase the evaluation in relation to the implementation of new treatment models (i.e., Dialectical Behavior Therapy - DBT, Acceptance and Commitment Therapy - ACT, Mindfulness, Functional Analytic Psychotherapy - FAP) as to better orientate prevention strategies. Further research is needed in gender sensitive interventions.

Psychiatric profiles in suicidal attempters: Relationships with suicide behaviour features.

BACKGROUND: Suicide constitutes a major health concern worldwide, being a significant contributor of death, globally. The diagnosis of a mental disorder has been extensively linked to the varying forms of suicidal ideation and behaviour. The aim of our study was to identify the varying diagnostic profiles in a sample of suicide attempters. METHODS: A sample of 683 adults (71.3% females, 40.10±15.74 years) admitted at a hospital emergency department due to a suicide attempt was recruited. Latent class analysis was used to identify diagnostic profiles and logistic regression to study the relationship between comorbidity profile membership and sociodemographic and clinical variables. RESULTS: Two comorbidity profiles were identified (Class I: low comorbidity class, 71.3% of attempters; Class II: high comorbidity class, 28.7% of attempters). Class I members were featured by the diagnosis of depression and general anxiety disorder, and low comorbidity; by contrast, the high comorbidity profile was characterized by a higher probability of presenting two or more coexisting psychiatric disorders. Class II included more females, younger, with more depressive symptoms and with higher impulsivity levels. Moreover, Class II members showed more severe suicidal ideation, higher number of suicide behaviours and a greater number of previous suicide attempts (p<.01, for all the outcomes), compared to Class I members. CONCLUSIONS: Psychiatric profiles may be considered for treatment provision and personalized psychiatric treatment in suicidal attempters as well as tackle suicide risk.

Heterogeneity in the trajectories of psychological distress among late adolescents during the COVID-19 pandemic.

Background: The coronavirus disease 2019 (COVID-19) pandemic has constrained opportunities in social, educational and professional domains, leading to developmental challenges for adolescents initiating their transition to adulthood. Meta-analysis indicated that there was a small increase in psychological distress during the first year of the COVID-19 pandemic. However, significant heterogeneity in the psychological response to the COVID-19 pandemic was noted. Developmental antecedents as well as social processes may account for such heterogeneity. The goal of this study was to characterize trajectories of psychological distress in late adolescence during the COVID-19 pandemic. Methods: 5014 late adolescents born between 2000 and 2002 from the UK Millennium Cohort Study completed online self-reported assessments at three occasions during the first year of the COVID-19 pandemic (May 2020, September/October 2020 and February/March 2021). These surveys assessed psychological distress, loneliness, social support, family conflict, as well as other pandemic stressors. Information on developmental antecedents were obtained when cohort members were 17 years of age. Results: Four distinct trajectories class were identified. Normative class (52.13%) experienced low and decreasing levels of psychological distress, while moderately increasing class (31.84%) experienced a small, but significant increase in distress over time and increasing class (8.75%) exhibited a larger increase in distress after the first wave of the pandemic. Inverted U-shaped class (7.29%) experienced elevated psychological distress during the first wave of the pandemic, followed by a decrease in distress in subsequent waves of the pandemic. Larger longitudinal increases in loneliness were noted among individuals in the elevated distress trajectory, compared to other trajectories. Pre-pandemic psychopathology was associated with elevated distress early in the pandemic. Conclusions: The largest trajectory showed low and declining psychological distress, highlighting the resilience of the majority of late adolescents. However, a subgroup of adolescents experienced large increases in psychological distress, identifying a group of individuals more vulnerable to pandemic-related stress.

Effects of elevated emotional symptoms on metabolic disease development: a 10-year follow-up study.

Background: In recent decades, the relationship between emotional disorders (i.e., depression and anxiety) and alterations in physiological functions (i.e., inflammation or metabolism) have been well supported. However, studies on a symptom-based approach have provided mixed results. Our study aims to gain insight into how subclinical statuses, featured by elevated depressive and/or anxious symptoms, may influence immunometabolic alterations in the concurrent relationship; and the development of metabolic diseases at 10-year follow-up: diabetes, hypertension and hypercholesterolemia. Methods: Data from 758 Greek adults [394 men (aged 41 ± 10 years) and 364 women (aged 37 ± 12 years)] were used. Four groups were created according to the levels of depressive and anxiety symptoms: (1) control group (CG), (2) depressive group (DG), (3) anxiety group (AG) and (4) depressive and anxiety group (DAG). Multi-indicator multi-causes (MIMIC) modeling was used to estimate metabolic function and inflammatory response scores, on a wide selection of blood biomarkers. Finally, a binary logistic regression was carried out to study the influence of symptoms on the development of the aforementioned metabolic diseases on a 10-year follow-up. Results: Group membership was not associated with metabolic function score. Conversely, DAG membership was related with higher inflammatory response score (B = 0.20, CI95 = 0.01, 0.40), with respect to the CG (p < 0.05). Both age and sex were significant variables in the calculation of both scores. Regarding disease at 10-year follow-up effect, risk of developing diabetes, hypertension and hypercholesterolemia was associated with age and socioeconomic status. Moreover, DG membership was significant for diabetes risk (OR = 2.08, CI95 = 1.00, 4.22) and DAG for hypercholesterolemia (OR = 1.68, CI95 = 1.16, 2.43). Limitations: Data on anti-inflammatory drugs and psychopharmacological medication were not collected in this study. Conclusions: Elevated symptoms of depression and anxiety accounts for inflammatory alterations at concurrent relationship and a higher risk of 10-year follow-up metabolic diseases.

Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning.

BACKGROUND: Although there is scientific evidence of the presence of immunometabolic alterations in major depression, not all patients present them. Recent studies point to the association between an inflammatory phenotype and certain clinical symptoms in patients with depression. The objective of our study was to classify major depression disorder patients using supervised learning algorithms or machine learning, based on immunometabolic and oxidative stress biomarkers and lifestyle habits. METHODS: Taking into account a series of inflammatory and oxidative stress biomarkers (C-reactive protein (CRP), tumor necrosis factor (TNF), 4-hydroxynonenal (HNE) and glutathione), metabolic risk markers (blood pressure, waist circumference and glucose, triglyceride and cholesterol levels) and lifestyle habits of the participants (physical activity, smoking and alcohol consumption), a study was carried out using machine learning in a sample of 171 participants, 91 patients with depression (71.42% women, mean age = 50.64) and 80 healthy subjects (67.50% women, mean age = 49.12). The algorithm used was the support vector machine, performing cross validation, by which the subdivision of the sample in training (70%) and test (30%) was carried out in order to estimate the precision of the model. The prediction of belonging to the patient group (MDD patients versus control subjects), melancholic type (melancholic versus non-melancholic patients) or resistant depression group (treatment-resistant versus non-treatment-resistant) was based on the importance of each of the immunometabolic and lifestyle variables. RESULTS: With the application of the algorithm, controls versus patients, such as patients with melancholic symptoms versus non-melancholic symptoms, and resistant versus non-resistant symptoms in the test phase were optimally classified. The variables that showed greater importance, according to the results of the area under the ROC curve, for the discrimination between healthy subjects and patients with depression were current alcohol consumption (AUC = 0.62), TNF-α levels (AUC = 0.61), glutathione redox status (AUC = 0.60) and the performance of both moderate (AUC = 0.59) and vigorous physical exercise (AUC = 0.58). On the other hand, the most important variables for classifying melancholic patients in relation to lifestyle habits were past (AUC = 0.65) and current (AUC = 0.60) tobacco habit, as well as walking routinely (AUC = 0.59) and in relation to immunometabolic markers were the levels of CRP (AUC = 0.62) and glucose (AUC = 0.58). In the analysis of the importance of the variables for the classification of treatment-resistant patients versus non-resistant patients, the systolic blood pressure (SBP) variable was shown to be the most relevant (AUC = 0.67). Other immunometabolic variables were also among the most important such as TNF-α (AUC = 0.65) and waist circumference (AUC = 0.64). In this case, sex (AUC = 0.59) was also relevant along with alcohol (AUC = 0.58) and tobacco (AUC = 0.56) consumption. CONCLUSIONS: The results obtained in our study show that it is possible to predict the diagnosis of depression and its clinical typology from immunometabolic markers and lifestyle habits, using machine learning techniques. The use of this type of methodology could facilitate the identification of patients at risk of presenting depression and could be very useful for managing clinical heterogeneity.

Risk factors for suicidal behaviour in late-life depression: A systematic review.

BACKGROUND: Suicide is a leading cause of preventable death worldwide, with its peak of maximum incidence in later life. Depression often puts an individual at higher risk for suicidal behaviour. In turn, depression deserves particular interest in old age due to its high prevalence and dramatic impact on health and wellbeing. AIM: To gather integrated evidence on the potential risk factors for suicide behaviour development in depressive older adults, and to examine the effects of depression treatment to tackle suicide behaviour in this population. METHODS: A systematic review of empirical studies, published from 2000 onwards, was conducted. Suicidal behaviour was addressed considering its varying forms (i.e., wish to die, ideation, attempt, and completed suicide). RESULTS: Thirty-five papers were selected for review, comprising both clinical and epidemiological studies. Most of studies focused on suicidal ideation (60%). The studies consistently pointed out that the risk was related to depressive episode severity, psychiatric comorbidity (anxiety or substance use disorders), poorer health status, and loss of functionality. Reduced social support and loneliness were also associated with suicide behaviour in depressive older adults. Finally, the intervention studies showed that suicidal behaviour was a robust predictor of depression treatment response. Reductions in suicidal ideation were moderated by reductions in risk factors for suicide symptoms. CONCLUSION: To sum up, common and age-specific risk factors seem to be involved in suicide development in depressive older adults. A major effort should be made to tackle this serious public health concern so as to promote older people to age healthily and well.

Relationship between immunometabolic status and cognitive performance among major depression disorder patients.

BACKGROUND: Alterations in cognitive performance have been described in patients with major depressive disorder (MDD). However, the specific risk factors of these changes are not yet known. This study aimed to explore whether inmunometabolic parameters are related to cognitive performance in MDD in comparison to healthy controls (HC) METHODS: Sample consisted of 84 MDD patients and 78 HC. Both groups were compared on the results of cognitive performance measured with the Cambridge Neuropsychological Test Automated Battery (CANTAB), the presence of metabolic syndrome (MetS) and an inflammatory/oxidative index calculated by a principal component analysis of peripheral biomarkers (tumor necrosis factor, C-reactive protein and 4-hydroxynonenal). A multiple linear regression was carried out, to study the relationship between inmunometabolic variables and the global cognitive performance, being the latter the dependent variable. RESULTS: Significant differences were obtained in the inflammatory/oxidative index between both groups (F(1157)= 12.93; p < .001), also in cognitive performance (F(1157)= 56.75; p < .001). The inmunometabolic covariate regression model (i.e., condition (HC/MDD), sex, age and medication loading, MetS, inflammatory/oxidative index and the interaction between MetS and inflammatory/oxidative index) was statistically significant (F(7157)= 11.24; p < .01) and explained 31% of variance. The condition, being either MDD or HD, (B=-0.97; p < .001), age (B=-0.28; p < .001) and the interaction between inflammatory/oxidative index and MetS (B=-0.38; p = .02) were factors associated to cognitive performance. LIMITATIONS: Sample size was relatively small. The cross-sectional design of the study limits the possibilities of analysis. CONCLUSIONS: Our results provide evidence on the conjoint influence of metabolic and inflammatory dysregulation on cognitive dysfunction in MDD patients. In this way, our study opens a line of research in immunometabolic agents to deal with cognitive decline associated with MDD.

Age at illness onset and physical activity are associated with cognitive impairment in patients with current diagnosis of major depressive disorder.

BACKGROUND: Cognitive impairment has been reported in patients with Major Depressive Disorder (MDD). This study aims to explore the association between lifestyle habits and health-related factors and the presence of cognitive symptoms in MDD patients. METHODS: Demographic, clinical, health-related variables and cognitive scores measured with the Cambridge Neuropsychological Test Automated Battery (CANTAB) were compared between 74 patients with current MDD and 68 healthy controls (HC). To test the hypothesis of associated factors to cognitive symptoms, multivariate backward stepwise linear regression models were run. RESULTS: Significant neuropsychological deficits were evident in MDD compared with HC in the global cognitive index (F=8.29; df=1, 140; p=0.005). In the regression analysis performed on MDD and HC, years of schooling (ß=-0.11; p=<0.001), job status (ß=-0.50; p=0.016), physical activity (ß=-0.25; p=0.04) and age at illness onset (ß=0.17; p=0.017) were statistically significant factors associated to cognitive impairment. The regression model ran in HC showed that only years of schooling were significant (ß=-0.07; p=<0.001) in this group. LIMITATIONS: Sample size was relatively small. Everyday cognitive skills were not evaluated. CONCLUSIONS: MDD patients have cognitive deficits. These deficits are linked with the years of education, job status, age of onset of the disease and the performance of physical activity. These results support the importance of the implementation of interventions targeting the cognitive reserve and lifestyle habits of MDD patients, in addition to the conventional therapeutic approach focused on symptoms control.

Intracellular calcium and inflammatory markers, mediated by purinergic stimulation, are differentially regulated in monocytes of patients with major depressive disorder.

The P2X7 receptor (P2X7R) is a ligand-gated ion channel that is being recognized as a major player in neuropsychiatric disorders such as Major Depressive Disorder (MDD). P2X7R activation is triggered by high extracellular ATP concentrations, leading to channel opening and inducing an increase in cytosolic calcium concentration ([Ca2+]c), that activates the inflammatory pathway. Those receptors are expressed not only in CNS cells but also in peripheral blood cells, where they are activated in response to inflammatory molecules such as bacterial lipopolysaccharide (LPS). LPS induced-tissue damage promotes an elevation of extracellular ATP, triggering the NRLP3-inflammasome assembly and activation that, sequentially, induces caspase-1 cleavage and IL-1ß processing and secretion. In this context, we attempt to understand the role of P2X7R in [Ca2+]c homeostasis regulation, inflammasome expression and its pharmacological modulation in MDD. For this purpose, monocytes were isolated from peripheral blood of MDD patients and [Ca2+]c was monitored with the intracellular probe Fura-2. Our results point out to P2X7R as the responsible of the Ca2+ imbalance, as well as TNF-α-dependent activation of caspase-1 in MDD patients. In addition, P2X7R blockade with its specific antagonist, JNJ-47965567, reduces the Ca2+ entry upon Bz-ATP exposure. Altogether, our results point that MDD patients have both, Ca2+ homeostasis alteration and an inflammatory status, which promote an independent-inflammasome activation of caspase-1. Therefore, we propose the pharmacological modulation of P2X7R as a therapeutic approach against MDD symptoms.

Inflammatory and metabolic disturbances are associated with more severe trajectories of late-life depression.

Late-life depression is a highly prevalent mental health condition with devastating consequences even from its earliest stages. Alterations in physiological functions, such as inflammatory and metabolic, have been described in patients with depression. However, little is known on the association between depression symptom course and metabolic and inflammation dysregulation. This study aimed to depict the course of depression symptoms while ageing, taking into consideration inter-individual heterogeneity. Moreover, it intended to study the associations between inflammatory and metabolic risk profiles and symptom trajectories. To do so, data from 13,203 adults aged 50-90 years (52.75% women; mean age at baseline = 65.07, SD = 10.00) were used. Blood sample and blood pressure measures were taken from 1536 participants (56.58% women; mean age at baseline = 61.73 years, sd = 7.64). Depression symptoms were assessed every two years across a 10-year follow-up. Trajectories were identified by means of latent class mixed modelling. Inflammation and metabolic risk profile scores were obtained from plasma and diagnostic-based indicators in the follow-up, using a robust latent-factor approach. Multigroup modelling was used to study the associations between the profiles and symptom trajectories. As a result, three heterogeneous trajectories of symptoms were identified (low-symptom, moderate-symptom and high-symptom trajectory). Participants depicting a high-symptom trajectory showed the greatest inflammation profile score and high metabolic risk. Moderate-symptom trajectory was also related to high inflammation and metabolic risk. To sum up, at-risk trajectories of symptoms were associated with high inflammation and risk of metabolic diseases. This study provides valuable evidence to advance personalised medicine and mental health precision, considering person-specific profiles and physiological concomitants.