Early exposure to cannabis and bipolar disorder incidence: Findings from a 22-year birth cohort study in Brazil.

BACKGROUND AND OBJECTIVES: Bipolar disorder is a chronic condition affecting millions of people worldwide. Currently, there is some evidence to suggest that cannabis use during adolescence may be an environmental risk factor for its onset, however inconsistencies have been observed across the literature. Considering this, we aimed to assess whether early lifetime cannabis is associated with subsequent bipolar disorder in young adults between 18 and 22 years of age. METHODS: Using data from the 1993 Pelotas (Brazil) birth cohort (n = 5249), cannabis exposure was examined at age 18 by self-report, and bipolar disorder diagnosis was measured at age 22 using the Mini International Neuropsychiatric Interview (MINI). In order to control the analysis, we considered socioeconomic status index, sex, skin color, physical abuse by parents and lifetime cocaine use. RESULTS: A total of 3781 individuals were evaluated in 2015 aged 22 years, of whom 87 were diagnosed with the bipolar disorder onset after the age of 18. Lifetime cannabis use predicted bipolar disorder onset at 22 years old (OR 1.82, 95% CI [1.10, 2.93]), and the effect remained after adjusting for socioeconomic status, sex, skin color, and physical abuse by parents (OR 2.00, 95% CI [1.20, 3.25]). However, this association was attenuated to statistically non-significant after further adjustment for all available covariates, including lifetime cocaine use (OR 1.79, 95% CI [0.95, 3.19]). We also found similar results for early cocaine use, where the association with bipolar disorder onset did not maintain significance in the multivariate model (OR 1.35, 95% CI [0.62, 2.86]). Otherwise, when we considered cannabis or cocaine lifetime use as a unique feature, our findings showed that the adolescent exposure to cannabis or cocaine increased the odds by 1.95 times of developing bipolar disorder at 22 years age, even when controlling for all other study variables (OR 2.14, 95% CI [1.30, 3.47]). Finally, our models suggest that cocaine use may potentially exert a major influence on the effect of lifetime cannabis use on bipolar disorder onset, and that physical abuse by parents and sex may modify the effect of cannabis use for later bipolar disorder onset. CONCLUSION: Based on our findings, early cannabis exposure predicted bipolar disorder onset in young adults, but this association was confounded by cocaine use. Contrary to schizophrenia, cannabis as a sole exposure was not associated with bipolar disorder onset after adjusting for control variables.

Treatment Approaches for Problematic Pornography Use: A Systematic Review.

Problematic pornography use (PPU) is an emerging condition associated with several negative psychological and sexual outcomes. This study aimed to systematically review treatment approaches for PPU. Potentially eligible studies were searched for in PubMed/MEDLINE, Embase, PsycINFO, and Web of Science up to April 1, 2023. The quality of the evidence was assessed with the use of the Joanna Briggs Institute's checklists, the Cochrane risk-of-bias tools, and the GRADE approach. A total of 8936 references were retrieved, and 28 studies were included in the systematic review (n = 500 participants). Included studies were case reports (k = 16), case series (k = 1), quasi-experimental investigations (k = 7), and randomized clinical trials (k = 4). The majority of included studies presented overall low quality and significant risk of bias, with all interventions receiving a low or very low rating according to the GRADE approach. Most studies investigated psychological interventions, with the predominance of second and third wave cognitive behavioral therapy interventions. Pharmacological treatments included opioid antagonists (naltrexone in most cases) and antidepressants, while one study investigated a protocol that included rTMS. Several investigations described the combination of psychological and pharmacological approaches. Most studies did not report on side effects, with online and self-help interventions presenting significant issues in terms of treatment adherence. Even though there are promising options that may have efficacy for the treatment of patients with PPU, the literature in the topic still presents significant limitations that compromise the reaching of more definitive conclusions.

Prevalence and risk factors of psychiatric symptoms and diagnoses before and during the COVID-19 pandemic: findings from the ELSA-Brasil COVID-19 mental health cohort.

BACKGROUND: There is mixed evidence on increasing rates of psychiatric disorders and symptoms during the coronavirus disease 2019 (COVID-19) pandemic in 2020. We evaluated pandemic-related psychopathology and psychiatry diagnoses and their determinants in the Brazilian Longitudinal Study of Health (ELSA-Brasil) São Paulo Research Center. METHODS: Between pre-pandemic ELSA-Brasil assessments in 2008-2010 (wave-1), 2012-2014 (wave-2), 2016-2018 (wave-3) and three pandemic assessments in 2020 (COVID-19 waves in May-July, July-September, and October-December), rates of common psychiatric symptoms, and depressive, anxiety, and common mental disorders (CMDs) were compared using the Clinical Interview Scheduled-Revised (CIS-R) and the Depression Anxiety Stress Scale-21 (DASS-21). Multivariable generalized linear models, adjusted by age, gender, educational level, and ethnicity identified variables associated with an elevated risk for mental disorders. RESULTS: In 2117 participants (mean age 62.3 years, 58.2% females), rates of CMDs and depressive disorders did not significantly change over time, oscillating from 23.5% to 21.1%, and 3.3% to 2.8%, respectively; whereas rate of anxiety disorders significantly decreased (2008-2010: 13.8%; 2016-2018: 9.8%; 2020: 8%). There was a decrease along three wave-COVID assessments for depression [ß = -0.37, 99.5% confidence interval (CI) -0.50 to -0.23], anxiety (ß = -0.37, 99.5% CI -0.48 to -0.26), and stress (ß = -0.48, 99.5% CI -0.64 to -0.33) symptoms (all ps < 0.001). Younger age, female sex, lower educational level, non-white ethnicity, and previous psychiatric disorders were associated with increased odds for psychiatric disorders, whereas self-evaluated good health and good quality of relationships with decreased risk. CONCLUSION: No consistent evidence of pandemic-related worsening psychopathology in our cohort was found. Indeed, psychiatric symptoms slightly decreased along 2020. Risk factors representing socioeconomic disadvantages were associated with increased odds of psychiatric disorders.

Inflammatory and oxidative stress markers in post-traumatic stress disorder: a systematic review and meta-analysis.

Post-traumatic stress disorder (PTSD) has been associated with persistent, low-degree inflammation, which could explain the increased prevalence of autoimmune conditions and accelerated aging among patients. The aim of the present study is to assess which inflammatory and oxidative stress markers are associated with PTSD. We carried out a meta-analytic and meta-regression analysis based on a systematic review of studies comparing inflammatory and oxidative stress markers between patients with PTSD and controls. We undertook meta-analyses whenever values of inflammatory and oxidative stress markers were available in two or more studies. Overall, 28,008 abstracts were identified, and 54 studies were included, with a total of 8394 participants. The Newcastle-Ottawa Quality Assessment Scale was used to evaluate the quality of the studies. Concentrations of C-reactive protein (SMD = 0.64; 95% CI: 0.21 to 1.06; p = 0.0031; k = 12), interleukin 6 (SMD = 0.94; 95% CI: 0.36 to 1.52; p = 0.0014; k = 32), and tumor necrosis factor-α (SMD = 0.89; 95% CI: 0.23 to 1.55; p = 0.0080; k = 24) were significantly increased in patients with PTSD in comparison with healthy controls. Interleukin 1ß levels almost reached the threshold for significance (SMD = 1.20; 95% CI: -0.04 to 2.44; p = 0.0569; k = 15). No oxidative stress marker was associated with PTSD. These findings may explain why PTSD is associated with accelerated aging and illnesses in which immune activation has a key role, such as cardiovascular diseases and diabetes. In addition, they pointed to the potential role of inflammatory markers as therapeutic targets.

Systematic Review of Psychiatric Adverse Effects Induced by Chloroquine and Hydroxychloroquine: Case Reports and Population Studies.

OBJECTIVE: To perform a systematic review on the psychiatric adverse effects of chloroquine (CQ) and hydroxychloroquine (HCQ); to summarize what is known about psychiatric adverse effects of these drugs; to compare clinical trials, populational studies, and case report studies; and to increase awareness of the potential psychiatric adverse effects of these drugs. DATA SOURCES: A literature search of PubMed, Scopus, and Web of Science was performed to identify manuscripts published between December 1962 and June 2022. Search terms included CQ, HCQ, psychiatry, psychosis, depression, anxiety, bipolar disorder, delirium, and psychotic disorders. STUDY SELECTION AND DATA EXTRACTION: Relevant studies included reports of adverse effects after CQ or HCQ ingestion. DATA SYNTHESIS: The current literature presents evidence for a risk of short-term psychiatric adverse effects induced by either CQ or HCQ. However, the populational-level studies presented some limitations regarding the voluntary response in survey data, self-report adverse effects, and placebo group reporting similar symptoms to the case group. Thus, populational-level studies addressing the discussed limitations and the nature and extent of possible psychiatric adverse effects are needed. RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE: Most of the patients who developed such adverse effects did not report a family history of psychiatric disease. The frequency of psychiatric adverse effects depends on the patient's biological sex, age, and body mass index, but not on the drug dosage. CONCLUSIONS: Based on clinical trials and case reports, the current literature presents evidence for a risk of short-term psychiatric adverse effects induced by either drug.

Predictors of suicide attempt in patients with obsessive-compulsive disorder: an exploratory study with machine learning analysis.

BACKGROUND: Patients with obsessive-compulsive disorder (OCD) are at increased risk for suicide attempt (SA) compared to the general population. However, the significant risk factors for SA in this population remains unclear - whether these factors are associated with the disorder itself or related to extrinsic factors, such as comorbidities and sociodemographic variables. This study aimed to identify predictors of SA in OCD patients using a machine learning algorithm. METHODS: A total of 959 outpatients with OCD were included. An elastic net model was performed to recognize the predictors of SA among OCD patients, using clinical and sociodemographic variables. RESULTS: The prevalence of SA in our sample was 10.8%. Relevant predictors of SA founded by the elastic net algorithm were the following: previous suicide planning, previous suicide thoughts, lifetime depressive episode, and intermittent explosive disorder. Our elastic net model had a good performance and found an area under the curve of 0.95. CONCLUSIONS: This is the first study to evaluate risk factors for SA among OCD patients using machine learning algorithms. Our results demonstrate an accurate risk algorithm can be created using clinical and sociodemographic variables. All aspects of suicidal phenomena need to be carefully investigated by clinicians in every evaluation of OCD patients. Particular attention should be given to comorbidity with depressive symptoms.

Prediction of suicide attempts in a prospective cohort study with a nationally representative sample of the US population.

BACKGROUND: There is still little knowledge of objective suicide risk stratification. METHODS: This study aims to develop models using machine-learning approaches to predict suicide attempt (1) among survey participants in a nationally representative sample and (2) among participants with lifetime major depressive episodes. We used a cohort called the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) that was conducted in two waves and included a nationally representative sample of the adult population in the United States. Wave 1 involved 43 093 respondents and wave 2 involved 34 653 completed face-to-face reinterviews with wave 1 participants. Predictor variables included clinical, stressful life events, and sociodemographic variables from wave 1; outcome included suicide attempt between wave 1 and wave 2. RESULTS: The model built with elastic net regularization distinguished individuals who had attempted suicide from those who had not with an area under the ROC curve (AUC) of 0.89, balanced accuracy 81.86%, specificity 89.22%, and sensitivity 74.51% for the general population. For participants with lifetime major depressive episodes, AUC was 0.89, balanced accuracy 81.64%, specificity 85.86%, and sensitivity 77.42%. The most important predictor variables were a diagnosis of borderline personality disorder, post-traumatic stress disorder, and being of Asian descent for the model in all participants; and previous suicide attempt, borderline personality disorder, and overnight stay in hospital because of depressive symptoms for the model in participants with lifetime major depressive episodes. Random forest and artificial neural networks had similar performance. CONCLUSIONS: Risk for suicide attempt can be estimated with high accuracy.

The Self-perception of Text message Dependence Scale (STDS): A Brazilian-Portuguese validation and expansion of its psychometric properties.

Text messaging is the primary form of technology-mediated interpersonal contact and the most carried out activity on cell phones. Despite its advantages, text messaging is not exempt from risks. The present paper aimed to validate and expand the psychometric properties of the Self-perception of Text-message Dependency Scale (STDS) in a Brazilian sample of adult internet users. In this cross-sectional study, we recruited a convenience sample of Brazilian internet users aged 18 and over. A total of 1,642 (M age = 38.6, SD = 13.5; 73% female) participants completed the STDS, the Mobile Phone Problem Usage Scale-27 (MPPUS), and the Problematic Internet Use Questionnaire - Short form - 9 questionnaires (PIUQ-SF-9). Multigroup confirmatory factor analysis showed measurement invariance for gender and age. Internal consistency was high when accessed by both McDonalds' Omega and Cronbach's alpha. Network Analysis provided insights into the core symptoms of problematic text messaging. Convergent validity of the STDS was demonstrated by the subscale's correlation with MPPUS and PIUQ-SF-9. Due to its expanded psychometric properties and brevity, the STDS can be used in more comprehensive investigations about other excessive technology-related behaviors, such as problematic smartphone and internet use, allowing a better understanding of the mechanisms involved in problematic technology use. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-022-02957-8.

Prediction of depression cases, incidence, and chronicity in a large occupational cohort using machine learning techniques: an analysis of the ELSA-Brasil study.

Abstract. BACKGROUND: Depression is highly prevalent and marked by a chronic and recurrent course. Despite being a major cause of disability worldwide, little is known regarding the determinants of its heterogeneous course. Machine learning techniques present an opportunity to develop tools to predict diagnosis and prognosis at an individual level. METHODS: We examined baseline (2008-2010) and follow-up (2012-2014) data of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), a large occupational cohort study. We implemented an elastic net regularization analysis with a 10-fold cross-validation procedure using socioeconomic and clinical factors as predictors to distinguish at follow-up: (1) depressed from non-depressed participants, (2) participants with incident depression from those who did not develop depression, and (3) participants with chronic (persistent or recurrent) depression from those without depression. RESULTS: We assessed 15 105 and 13 922 participants at waves 1 and 2, respectively. The elastic net regularization model distinguished outcome levels in the test dataset with an area under the curve of 0.79 (95% CI 0.76-0.82), 0.71 (95% CI 0.66-0.77), 0.90 (95% CI 0.86-0.95) for analyses 1, 2, and 3, respectively. CONCLUSIONS: Diagnosis and prognosis related to depression can be predicted at an individual subject level by integrating low-cost variables, such as demographic and clinical data. Future studies should assess longer follow-up periods and combine biological predictors, such as genetics and blood biomarkers, to build more accurate tools to predict depression course.

Differential biomarker signatures in unipolar and bipolar depression: A machine learning approach.

OBJECTIVE: This study used machine learning techniques combined with peripheral biomarker measurements to build signatures to help differentiating (1) patients with bipolar depression from patients with unipolar depression, and (2) patients with bipolar depression or unipolar depression from healthy controls. METHODS: We assessed serum levels of interleukin-2, interleukin-4, interleukin-6, interleukin-10, tumor necrosis factor-α, interferon-γ, interleukin-17A, brain-derived neurotrophic factor, lipid peroxidation and oxidative protein damage in 54 outpatients with bipolar depression, 54 outpatients with unipolar depression and 54 healthy controls, matched by sex and age. Depressive symptoms were assessed using the Hamilton Depression Rating Scale. Variable selection was performed with recursive feature elimination with a linear support vector machine kernel, and the leave-one-out cross-validation method was used to test and validate our model. RESULTS: Bipolar vs unipolar depression classification achieved an area under the receiver operating characteristics (ROC) curve (AUC) of 0.69, with 0.62 sensitivity and 0.66 specificity using three selected biomarkers (interleukin-4, thiobarbituric acid reactive substances and interleukin-10). For the comparison of bipolar depression vs healthy controls, the model retained five variables (interleukin-6, interleukin-4, thiobarbituric acid reactive substances, carbonyl and interleukin-17A), with an AUC of 0.70, 0.62 sensitivity and 0.7 specificity. Finally, unipolar depression vs healthy controls comparison retained seven variables (interleukin-6, Carbonyl, brain-derived neurotrophic factor, interleukin-10, interleukin-17A, interleukin-4 and tumor necrosis factor-α), with an AUC of 0.74, a sensitivity of 0.68 and 0.70 specificity. CONCLUSION: Our findings show the potential of machine learning models to aid in clinical practice, leading to more objective assessment. Future studies will examine the possibility of combining peripheral blood biomarker data with other biological data to develop more accurate signatures.

Reduced Neurite Density in Neuronal Cell Cultures Exposed to Serum of Patients with Bipolar Disorder.

BACKGROUND: Increased inflammatory markers and oxidative stress have been reported in serum among patients with bipolar disorder (BD). The aim of this study is to assess whether biochemical changes in the serum of patients induces neurotoxicity in neuronal cell cultures. METHODS: We challenged the retinoic acid-differentiated human neuroblastoma SH-SY5Y cells with the serum of BD patients at early and late stages of illness and assessed neurite density and cell viability as neurotoxic endpoints. RESULTS: Decreased neurite density was found in neurons treated with the serum of patients, mostly patients at late stages of illness. Also, neurons challenged with the serum of late-stage patients showed a significant decrease in cell viability. CONCLUSIONS: Our findings showed that the serum of patients with bipolar disorder induced a decrease in neurite density and cell viability in neuronal cultures.

Withdrawal-like Symptoms in Problematic Pornography Use: A Scoping Review.

OBJECTIVES: There is still debate whether problematic pornography use (PPU) can be understood as an addictive disorder, and little is known about withdrawal-like symptoms in these patients. Therefore, our aim was to summarize the existing scientific literature about this topic in the format of a scoping review. METHODS: Potentially eligible references were retrieved from 6 databases (MEDLINE/PubMed, Web of Science, Embase, PsycINFO, LILACS, and SciELO) up to February 7, 2023. Studies were included if they reported original data about withdrawal-like symptoms in individuals with PPU. Studies investigating child pornography use, or in which it was not possible to extract data of interest were excluded. Data extracted from included studies and their quality assessment were described narratively. RESULTS: A total of 937 references were retrieved, and 14 studies were included (n = 31,009 participants). According to our results, cravings were intense in most cases and frequent reasons for relapse. The proportion of participants experiencing other withdrawal-like symptoms (mental, sexual, and physical symptoms) reached up to 72.2% in the cross-sectional studies with this information available. Severity of withdrawal symptoms was associated with severity of PPU and frequency of pornography use. In some studies, measures of withdrawal symptoms were considered central symptoms of PPU and were significantly higher in individuals considering treatment. Masturbation was reported to alleviate withdrawal-like symptoms in some cases. CONCLUSIONS: There is preliminary evidence for the existence of withdrawal-like symptoms in PPU. Future studies should investigate onset, characteristics, duration, and proportion of withdrawal-like symptoms in PPU with robust methodology, also investigating potential confounding variables.

Premorbid intelligence quotient and school failure as risk markers for bipolar disorder and major depressive disorder.

Mood disorders significantly impact global health, with MDD ranking as the second leading cause of disability in the United States and BD ranking 18th. Despite their prevalence and impact, the relationship between premorbid intelligence and the subsequent development of BD and MDD remains inconclusive. This study investigates the potential of premorbid Intelligence Quotient (IQ) and school failure frequency as risk factors for Bipolar Disorder (BD) and Major Depressive Disorder (MDD) in a birth cohort setting. We analyze data from the Pelotas population-based birth cohort study, comprising 3580 participants aged 22, who had no prior mood disorder diagnoses. Utilizing regression models and accounting for potential confounders, we assess the impact of IQ and school failure, measured at age 18, on the emergence of BD and MDD diagnoses at age 22, using individuals without mood disorders as comparators. Results reveal that lower IQ (below 70) at 18 is associated with an increased risk of BD (Adjusted Odds Ratio [AOR] 1.75, 95%CI: 1.00-3.09, p < 0.05), while higher IQ (above 120) is linked to MDD (AOR 2.16, 95%CI: 1.24-3.75, p < 0.001). Moreover, an elevated number of school failures is associated with increased BD risk (AOR 1.23, 95%CI: 1.11-1.41, p < 0.001), particularly for BD type 1 (AOR 1.36, 95% CI: 1.17-1.58, p < 0.001). These findings offer insights into the distinct premorbid intellectual characteristics of BD and MDD and contribute to a deeper understanding of their developmental trajectories, potentially informing the development of risk assessment tools for mood disorders.

Perinatal factors as risk factors of bipolar disorder onset in young adulthood: a 22-year birth cohort.

BACKGROUND: Bipolar disorder (BD) is a leading cause of disability-adjusted life years in young adults. Complications during prenatal periods have been associated with BD previously. The study aims to examine the association between perinatal factors and BD in order to prevent the risk of developing BD. METHODS: 3,794 subjects from the 1993 Pelotas population-based birth cohort study were included. We assessed 27 initial variables at birth and modelled BD onset at 18 and 22 years. We performed bivariate analysis, using binomial logistic regression models. The variables with p-value smaller than 0.05 were included into a multiple regression with confounding variables. RESULTS: Maternal smoking was associated with a 1.42-fold increased risk of BD at 18 or 22 years old (95% CI: 1.091-1.841), and maternal passive exposure to tobacco with a 1.43-fold increased risk (95% CI: 1.086-1.875). No association was found between other perinatal factors and BD after controlling for confounding factors. CONCLUSION: The results of this cohort corroborate with previous findings in the literature that already indicate the negative outcomes of maternal smoking during pregnancy. They may now be linked to other studies to target these factors for preventing the development of BD.

Exploring protective factors in a high-risk subsample: the pivotal role of paternal support in preventing depression in a cohort of young adults.

BACKGROUND: Major depressive disorder (MDD) is a global concern due to its widespread prevalence and morbidity. Identifying protective factors in high-risk individuals, including those with a familial predisposition, maltreatment history, and socio-economic vulnerabilities, is crucial. METHODS: We assessed a high-risk subsample within a young adult population cohort (n = 791; mean age = 31.94 [SD = 2.18]) across three waves. Using multiple regression models to analyse higher education, feeling supported, spirituality, psychotherapy access, higher socioeconomic status, involvement in activities, cohabitation, and family unity in Waves 1 and 2, and their association with MDD resilience at Wave 3. RESULTS: In the high-risk group, MDD incidence was 13.7% (n=24). Paternal support had a protective effect on MDD incidence (OR = 0.366; 95% CI [0.137 to 0.955], p = 0.040) and suicidal attempt risk (OR = 0.380; 95% CI [0.150 to 0.956], p = 0.038). Higher resilience scores were also protective (OR = 0.975; 95% CI [0.953 to 0.997], p = 0.030), correlating with reduced BDI (r = 0.0484; B = -0.2202; 95% CI [-0.3572 to -0.0738]; p = 0.003) and MADRS scores (r = 0.0485; B = -0.2204; 95% CI [-0.3574 to -0.0741]; p = 0.003). CONCLUSIONS: Our paper emphasizes reorienting the MDD approach, focusing on positive prevention strategies. It highlights fathers' crucial role in family-based interventions and promoting resilience in high-risk populations.

Protective factors against depression in high-risk children and adolescents: a systematic review of longitudinal studies.

BACKGROUND: Depression is a significant global disability, and early adverse experiences (EAE) represent consistent risk factors in children. However, protective factors play a vital role in promoting healthy development and mitigating these risks. METHODS: We conducted a thorough literature search on Pubmed, APA, Emcare, and EMBASE from 1946 to August 25, 2023. We included longitudinal studies analyzing protective factors for depressive symptoms in high-risk children or adolescents, excluding cross-sectional studies, reviews, and pre-clinical studies. OUTCOMES: Our analysis comprised 29 studies with 62,405 participants, identifying 38 protective factors. Positive individual characteristics, family factors, peer relationships, school-related aspects, neighborhood characteristics and intrinsic religiosity were associated with reduced depressive outcomes. INTERPRETATION: These findings have important implications for developing preventive strategies in this population. Addressing protective factors can contribute to preventing depression and enhancing mental well-being across the lifespan.