Persistent cannabis dependence and alcohol dependence represent comparable risks for midlife economic and social problems::A longitudinal cohort study

With the increasing legalization of cannabis, understanding the consequences of cannabis use is particularly timely. We examined the association between cannabis use and dependence, prospectively assessed between ages 18–38, and economic and social problems at age 38. We studied participants in the Dunedin Longitudinal Study, a cohort (n=1,037) followed from birth to age 38. Study members with regular cannabis use and persistent dependence experienced downward socioeconomic mobility, more financial difficulties, workplace problems, and relationship conflict in early midlife. Cannabis dependence was not linked to traffic-related convictions. Associations were not explained by socioeconomic adversity, childhood psychopathology, achievement orientation, or family structure; cannabis-related criminal convictions; early onset of cannabis dependence; or comorbid substance dependence. Cannabis dependence was associated with more financial difficulties than alcohol dependence; no difference was found in risks for other economic or social problems. Cannabis dependence is not associated with fewer harmful economic and social problems than alcohol dependence

Etiology of pervasive versus situational antisocial behaviors:a multi-informant longitudinal cohort study

The aim of this study was to disentangle pervasive from situational antisocial behaviors using multiple informants, and to investigate their genetic and environmental etiologies in preadolescence and across time. Antisocial behaviors were assessed in 2,232 twins from the Environmental Risk (E‐Risk) Longitudinal Twin Study at ages 5 and 12. Pervasive antisocial behaviors were defined as behaviors that mothers, teachers, interviewers, and twins themselves agreed on. Results from a psychometric model indicated that the variation in children's pervasive antisocial behaviors was mostly accounted for by familial influences that originated in childhood, whereas situational behaviors were explained by newly emerging nonshared environmental and genetic influences. This study shows that children's pervasive and situational antisocial behaviors have distinct etiologies that could guide research and treatment

A risk calculator to predict adult Attention-deficit/Hyperactivity disorder::Generation and external validation in three birth cohorts and one clinical sample.

AimFew personalised medicine investigations have been conducted for mental health. We aimed to generate and validate a risk tool that predicts adult attention-deficit/hyperactivity disorder (ADHD).MethodsUsing logistic regression models, we generated a risk tool in a representative population cohort (ALSPAC - UK, 5113 participants, followed from birth to age 17) using childhood clinical and sociodemographic data with internal validation. Predictors included sex, socioeconomic status, single-parent family, ADHD symptoms, comorbid disruptive disorders, childhood maltreatment, ADHD symptoms, depressive symptoms, mother's depression and intelligence quotient. The outcome was defined as a categorical diagnosis of ADHD in young adulthood without requiring age at onset criteria. We also tested Machine Learning approaches for developing the risk models: Random Forest, Stochastic Gradient Boosting and Artificial Neural Network. The risk tool was externally validated in the E-Risk cohort (UK, 2040 participants, birth to age 18), the 1993 Pelotas Birth Cohort (Brazil, 3911 participants, birth to age 18) and the MTA clinical sample (USA, 476 children with ADHD and 241 controls followed for 16 years from a minimum of 8 and a maximum of 26 years old).ResultsThe overall prevalence of adult ADHD ranged from 8.1 to 12% in the population-based samples, and was 28.6% in the clinical sample. The internal performance of the model in the generating sample was good, with an area under the curve (AUC) for predicting adult ADHD of 0.82 (95% confidence interval (CI) 0.79-0.83). Calibration plots showed good agreement between predicted and observed event frequencies from 0 to 60% probability. In the UK birth cohort test sample, the AUC was 0.75 (95% CI 0.71-0.78). In the Brazilian birth cohort test sample, the AUC was significantly lower -0.57 (95% CI 0.54-0.60). In the clinical trial test sample, the AUC was 0.76 (95% CI 0.73-0.80). The risk model did not predict adult anxiety or major depressive disorder. Machine Learning approaches did not outperform logistic regression models. An open-source and free risk calculator was generated for clinical use and is available online at https://ufrgs.br/prodah/adhd-calculator/.ConclusionsThe risk tool based on childhood characteristics specifically predicts adult ADHD in European and North-American population-based and clinical samples with comparable discrimination to commonly used clinical tools in internal medicine and higher than most previous attempts for mental and neurological disorders. However, its use in middle-income settings requires caution

Identifying adolescents at risk for depression: a prediction score performance in cohorts based in three different continents:A Prediction Score Performance in Cohorts Based in 3 Different Continents

OBJECTIVE: Prediction models have become frequent in the medical literature, but most published studies are conducted in a single setting. Heterogeneity between development and validation samples has been posited as a major obstacle for the generalization of models. We aimed to develop a multivariable prognostic model using sociodemographic variables easily obtainable from adolescents at age 15 to predict a depressive disorder diagnosis at age 18 and to evaluate its generalizability in 2 samples from diverse socioeconomic and cultural settings. METHOD: Data from the 1993 Pelotas Birth Cohort were used to develop the prediction model, and its generalizability was evaluated in 2 representative cohort studies: the Environmental Risk (E-Risk) Longitudinal Twin Study and the Dunedin Multidisciplinary Health and Development Study. RESULTS: At age 15, 2,192 adolescents with no evidence of current or previous depression were included (44.6% male). The apparent C-statistic of the models derived in Pelotas ranged from 0.76 to 0.79, and the model obtained from a penalized logistic regression was selected for subsequent external evaluation. Major discrepancies between the samples were identified, impacting the external prognostic performance of the model (Dunedin and E-Risk C-statistics of 0.63 and 0.59, respectively). The implementation of recommended strategies to account for this heterogeneity among samples improved the model’s calibration in both samples. CONCLUSION: An adolescent depression risk score comprising easily obtainable predictors was developed with good prognostic performance in a Brazilian sample. Heterogeneity among settings was not trivial, but strategies to deal with sample diversity were identified as pivotal for providing better risk stratification across samples. Future efforts should focus on developing better methodological approaches for incorporating heterogeneity in prognostic research

Is Toxoplasma Gondii Infection Related to Brain and Behavior Impairments in Humans? Evidence from a Population-Representative Birth Cohort

BACKGROUND:Toxoplasma gondii (T. gondii) is a protozoan parasite present in around a third of the human population. Infected individuals are commonly asymptomatic, though recent reports have suggested that infection might influence aspects of the host's behavior. In particular, Toxoplasma infection has been linked to schizophrenia, suicide attempt, differences in aspects of personality and poorer neurocognitive performance. However, these studies are often conducted in clinical samples or convenience samples. METHODS/RESULTS:In a population-representative birth-cohort of individuals tested for presence of antibodies to T. gondii (N = 837) we investigated the association between infection and four facets of human behavior: neuropsychiatric disorder (schizophrenia and major depression), poor impulse control (suicidal behavior and criminality), personality, and neurocognitive performance. Suicide attempt was marginally more frequent among individuals with T. gondii seropositivity (p = .06). Seropositive individuals also performed worse on one out of 14 measures of neuropsychological function. CONCLUSION:On the whole, there was little evidence that T. gondii was related to increased risk of psychiatric disorder, poor impulse control, personality aberrations or neurocognitive impairment

Social Isolation, Loneliness and Depression in Young Adulthood: A Behavioural Genetic Analysis

PURPOSE: To investigate the association between social isolation and loneliness, how they relate to depression, and whether these associations are explained by genetic influences. METHODS: We used data from the age-18 wave of the Environmental Risk Longitudinal Twin Study, a birth cohort of 1116 same-sex twin pairs born in England and Wales in 1994 and 1995. Participants reported on their levels of social isolation, loneliness and depressive symptoms. We conducted regression analyses to test the differential associations of isolation and loneliness with depression. Using the twin study design, we estimated the proportion of variance in each construct and their covariance that was accounted for by genetic and environmental factors. RESULTS: Social isolation and loneliness were moderately correlated (r = 0.39), reflecting the separateness of these constructs, and both were associated with depression. When entered simultaneously in a regression analysis, loneliness was more robustly associated with depression. We observed similar degrees of genetic influence on social isolation (40 %) and loneliness (38 %), and a smaller genetic influence on depressive symptoms (29 %), with the remaining variance accounted for by the non-shared environment. Genetic correlations of 0.65 between isolation and loneliness and 0.63 between loneliness and depression indicated a strong role of genetic influences in the co-occurrence of these phenotypes. CONCLUSIONS: Socially isolated young adults do not necessarily experience loneliness. However, those who are lonely are often depressed, partly because the same genes influence loneliness and depression. Interventions should not only aim at increasing social connections but also focus on subjective feelings of loneliness