Aspectos neurodesenvolvimentais do transtorno bipolar em uma coorte populacional de nascimento

Mundialmente, o Transtorno Bipolar (TB) é sexta causa de incapacidade. Entre os primeiros sintomas de humor e o diagnóstico formal, as pessoas com TB demoram cerca de seis anos para receber o tratamento adequado. Assim, objetivou-se desenvolver um modelo preditivo de indivíduos que desenvolverão TB aos 22 anos de idade usando algoritmos de inteligência artificial (machine learning). Foram acompanhadas 3.748 pessoas ao nascer e aos 11, 15, 18 e 22 anos de idade em uma coorte de nascimentos da cidade de Pelotas, RS, Brasil. Utilizou-se o algoritmo elastic net com 10-fold cross- validation para predizer quais indivíduos desenvolverão TB aos 22 anos de idade, usando as variáveis coletadas do nascimento até aos 18 anos de idade. Posteriormente, foi desenvolvido um modelo de estratificação de risco de pessoas com TB. Um total de 107 (2,8%) indivíduos foi diagnosticado com TB tipo I (TB-I), 26 (0.6%) participantes com o tipo II (TB-II) e 87 (2,3%) pessoas com o tipo não-especificado. O modelo com as variáveis coletadas aos 18 anos de idade foi o que alcançou melhores medidas de desempenho: área sob a curva ROC (AUC) de 0,82 (95% IC, 0,75– 0,88), acurácia balanceada de 0,75, sensibilidade de 0,72 e especificidade de 0,77. As variáveis mais importantes foram risco de suicídio, transtorno de ansiedade generalizada e abuso físico parental. Além disso, o subgrupo com alto risco para TB apresentou uma alta freqüência para consumo de drogas e sintomas depressivos. A detecção precoce de TB usando variáveis clínicas e sociodemográficas pode ser clinicamente relevante para intervir precocemente e prevenir o curso pernicioso do transtorno. O quociente de inteligência (QI) e o número de reprovações escolares podem ser importantes marcadores clínicos de neurodesenvolvimento para identificação do TB e essa associação permanece controversa na literatura. Objetivou-se identificar o QI e o número de reprovações escolares como fatores de risco para TB antes do diagnóstico formal em um estudo de coorte de nascimentos. Foi incluído 3580 participantes do estudo de coorte de nascimentos de base populacional de Pelotas na coleta de dados aos 22 anos e, na coleta anterior, nenhum sujeito deveria ter diagnóstico prévio de transtorno de humor. Foi realizado modelos de regressão controlando potenciais confundidores para avaliar o impacto do QI e do número de reprovações escolares obtido aos 18 anos em um diagnóstico subsequente de TB e Transtorno Depressivo Maior (TDM) aos 22 anos, comparando indivíduos sem transtornos de humor como comparadores. Encontrou-se que ter um QI baixo e limítrofe (abaixo de 70) aos 18 anos foi um marcador de risco para participantes com TB (Razão de Chance Ajustado [AOR] 1,75, IC 95%: 1,00–3,09, p<0,05) e QI superior (acima de 120) para indivíduos com TDM (AOR 2,16, IC 95%: 1,24–3,75, p<0,001). O número de reprovações escolares aumentou o risco de TB (AOR 1,23, IC 95%: 1,11–1,41, p<0,001), mas não para indivíduos com TDM. O número de reprovações escolares foi um significativo marcador para TB-I (AOR 1,36, IC 95%: 1,17–1,58, p<0,001), porém não em indivíduos com TB-II ou sem transtorno de humor. Os resultados sugerem o TB tem um desempenho intelectual pré-mórbido característico. Estes achados podem contribuir para a compreensão da fisiopatologia do TB e seu curso neurodesenvolvimental, auxiliando no desenvolvimento de ferramentas para sua detecção precoce.Bipolar Disorder (BD) is the sixth leading cause of disability worldwide. It takes about six years for people with BD to receive adequate treatment. Thus, the objective was to develop a predictive model of individuals who will develop BD at 22 years of age using data from a birth cohort through machine learning algorithms. A total of 3,748 participants were followed at birth and 11, 15, 18, and 22 years of age. The elastic net algorithm with 10-fold cross-validation was used to predict which individuals will develop BD at 22 years of age, using variables collected from birth to age of 18 years. Subsequently, a risk stratification model for subjects with BD was developed. A total of 107 (2.8%) individuals were diagnosed with BD type I (BD-I), 26 (0.6%) participants with type II (BD-II), and 87 (2.3%) people with type not otherwise specified. The model with the variables collected at 18 years of age was the one that achieved the best performance measures: area under the ROC curve (AUC) of 0.82 (95% CI, 0.75–0.88), balanced accuracy of 0.75, sensitivity of 0.72, and specificity of 0.77. The most important variables were suicide risk, generalized anxiety disorder, and parental physical abuse. In addition, the subgroup at high risk for TB had a high frequency of drug use and depressive symptoms. Early detection of TB utilizing clinical and sociodemographic variables may be clinically relevant to intervene early and prevent the pernicious course of the disorder. The intelligence quotient (IQ) and the number of school failures may be important clinical neurodevelopmental markers for identifying BD. This association remains controversial in the literature. The objective was to identify IQ and the number of school failures as risk factors for BD before formal diagnosis in a birth cohort study. A total of 3580 participants from the Pelotas population-based birth cohort study were included in the data collection at the age of 22 years, and no subject should have had a previous diagnosis of mood disorder in a previous follow-up visit. Regression models controlling for potential confounders were performed to assess the impact of IQ and the number of school failures obtained at age 18 on a diagnosis of BD and Major Depressive Disorder (MDD) at age 22, comparing individuals without mood disorders. Having a low and borderline IQ (below 70) at age 18 was a risk marker for participants with BD (Adjusted Odds Ratio [AOR] 1.75, 95% CI: 1.00–3.09, p<0.05) and higher IQ (above 120) for individuals with MDD (AOR 2.16, 95% CI: 1.24–3.75, p<0.001). The number of school failures increased the risk for BD (AOR 1.23, 95% CI: 1.11–1.41, p<0.001), but not for individuals with MDD. The number of school failures was a significant marker for BD-I (AOR 1.36, 95% CI: 1.17–1.58, p<0.001), but not in individuals with BD-II or without a mood disorder. The results suggest that BD has a characteristic premorbid intellectual performance. These findings may contribute to understanding the pathophysiology of BD and its neurodevelopmental course, aiding in developing tools for early detection

Preditores de heterogeneidade cognitiva no transtorno bipolar : uma abordagem machine-learning

Objetivos: Identificar os preditores de heterogeneidade cognitiva nos sujeitos com Transtorno Bipolar (TB). Métodos: Foi recrutado 142 sujeitos com TB em atendimento ambulatorial e 100 voluntários sem transtornos psiquiátricos do Brasil e da Espanha para realizar avaliação neuropsicológica. Foi realizado Análise de Cluster Hierárquica e Análise de Função Discriminante para determinar e confirmar os subgrupos cognitivos. Por fim, foi usado o algoritmo Classification and Regression Tree (CART) para identificar os preditores dos subgrupos cognitivos anteriormente estabelecidos. Resultados: Foi observado a presença de três clusters cognitivos: indivíduos cognitivamente intacto (38%), seletivamente prejudicados (38%) e globalmente prejudicados (21%). Os preditores mais importantes foram anos de educação, anos de doença, número de hospitalizações, idade e idade de início. Conclusão: Os resultados corroboram com recentes achados sobre a heterogeneidade cognitiva nos sujeitos com TB. Além disso, os presentes achados indicam uma sobreposição entre aspectos neurodesenvolvimentais e história de doença.Objective: We aimed to determine predictors of cognitive heterogeneity in subjects with Bipolar Disorder (BD). Methods: We recruited 142 outpatients with Bipolar Disorder and 100 unaffected volunteers from Brazil and Spain that underwent a neuropsychological assessment. We performed Hierarchical Cluster Analysis and Discriminant Function Analysis to identify and validate cognitive subgroups, respectively. Then, we used Classification and Regression Tree (CART) algorithm to determine predictors of the cognitive clusters. Results: We identified three cognitive clusters in BD: intact (38%), selectively impaired (38%), and globally impaired subjects (21%). The most important predictors of cognitive subgroups were years of education, years of disease, the number of hospitalizations, age, and age of onset, respectively. Conclusion: These results corroborate with recent findings of neuropsychological heterogeneity in Bipolar Disorder. Furthermore, the present findings suggest overlapping between neurodevelopmental and morbid aspects

Precision psychiatry : the future is now

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Multiple clinical risks for cannabis users during the COVID-19 pandemic

The pandemic caused by Sars-CoV-2 (COVID-19) has been a great concern for public and mental health systems worldwide. The identification of risk groups is essential for the establishment of preventive and therapeutic strategies, as for substance users. During COVID-19 pandemic, there was an increase in the use of psychoactive substances during the lockdown, including cannabis. This commentary reviews relevant findings and discusses scientific evidence on the risks of worse clinical and psychiatric complications due to coronavirus disease COVID-19 in subjects who use cannabis. Although they are not included as a risk group in the health recommendations for that disease, they may have a more vulnerable respiratory system to viral diseases. There are certain similarities between the harmful cardiovascular and respiratory effects of cannabis use and those of smoking. Due to the different modes of smoking, cannabis chemicals are retained in the body for longe and may also contain other toxic substances such as tar, a substance found in tobacco and which has been associated with the development of lung cancer, bronchitis and pulmonary emphysema. Therefore, we discuss if individuals who use cannabis regularly might be more vulnerable to COVID-19 infection. This population deserves more clinical attention worldwide and this manuscript can help clinicians become more aware of cannabis risks during pandemics and develop specific intervention strategies

High COVID-19 morbidity and mortality risk among smoked drug users in Brazil

In much of the West, including Brazil, drug use has increased since social distancing began in response to the pandemic. Use of smoked and modified drugs, and their impacts on health, may contribute to aggravate the effects of the pandemic. However, studies on the relationship between use of smoked drugs and the new coronavirus are still scarce and have not received enough attention in global health recommendations. This paper aims to briefly review the relationship between use of smoked drugs and acute respiratory syndrome coronavirus 2 [SARS-CoV-2]. Recent studies also suggest that drug consumption increases the risk of contamination by SARS-CoV-2 and leads to worse prognosis, particularly consumption of drugs that affect lung function. Use of smoked drugs, especially tobacco, is strongly associated with lung diseases that are risk factors for contamination by SARS-CoV-2. It is essential to develop strategies based on specific characteristics of drug users and for mental health professionals to be included in strategic teams. It is also necessary to invest in information campaigns regarding risks and prevention of harm caused by smoked drugs as well as to design strategies that facilitate access to psychosocial treatment during the pandemic

Aberrant IL-17 levels in rodent models of Autism Spectrum Disorder:a systematic review

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder characterised by stereotyped behaviours, specific interests, and impaired communication skills. Elevated levels of pro-inflammatory cytokines, such as interleukin-17A (IL-17A or IL-17), have been implicated as part of immune alterations that may contribute to this outcome. In this context, rodent models have helped elucidate the role of T-cell activation and IL-17 secretion in the pathogenesis of ASD. Regarding the preclinical findings, the data available is contradictory in offspring but not in the pregnant dams, pointing to IL-17 as one of the main drivers of altered behaviour in some models ASD, whilst there are no alterations described in IL-17 levels in others. To address this gap in the literature, a systematic review of altered IL-17 levels in rodent models of ASD was conducted. In total, 28 studies that explored IL-17 levels were included and observed that this cytokine was generally increased among the different models of ASD. The data compiled in this review can help the choice of animal models to study the role of cytokines in the development of ASD, seeking a parallel with immune alterations observed in individuals with this condition. Systematic Review Registration: PROSPERO, identifier CRD42022306558

Identifying pathways between psychiatric symptoms and psychosocial functioning in the general population

The present study aims to identify pathways between psychiatric network symptoms and psychosocial functioning and their associated variables among functioning clusters in the general population. A cross-sectional web-based survey was administered in a total of 3,023 individuals in Brazil. The functioning clusters were derived by a previous study identifying three different groups based on the online Functioning Assessment Short Test. Networking analysis was fitted with all items of the Patient-Reported Outcomes Measurement Information System for depression and for anxiety (PROMIS) using the mixed graphical model. A decision tree model was used to identify the demographic and clinical characteristics of good and low functioning. A total of 926 (30.63%) subjects showed good functioning, 1,436 (47.50%) participants intermediate functioning, and 661 (21.86%) individuals low functioning. Anxiety and uneasy symptoms were the most important nodes for good and intermediate clusters but anxiety, feeling of failure, and depression were the most relevant symptoms for low functioning. The decision tree model was applied to identify variables capable to discriminate individuals with good and low functioning. The algorithm achieved balanced accuracy 0.75, sensitivity 0.87, specificity 0.63, positive predictive value 0.63 negative predictive value 0.87 (p<0.001), and an area under the curve of 0.83 (95%CI:0.79–0.86, p<0.01). Our results show that individuals who present psychological distress are more likely to experience poor functional status, suggesting that this subgroup should receive a more comprehensive psychiatric assessment and mental health care