BackgroundPredicting which children will go on to develop mental health symptoms as adolescents is critical for early intervention and preventing future, severe negative outcomes. Although many aspects of a child's life, personality, and symptoms have been flagged as indicators, there is currently no model created to screen the general population for the risk of developing mental health problems. Additionally, the advent of machine learning techniques represents an exciting way to potentially improve upon the standard prediction modelling technique, logistic regression. Therefore, we aimed to I.) develop a model that can predict mental health problems in mid-adolescence II.) investigate if machine learning techniques (random forest, support vector machines, neural network, and XGBoost) will outperform logistic regression. MethodsIn 7,638 twins from the Child and Adolescent Twin Study in Sweden we used 474 predictors derived from parental report and register data. The outcome, mental health problems, was determined by the Strengths and Difficulties Questionnaire. Model performance was determined by the area under the receiver operating characteristic curve (AUC). ResultsAlthough model performance varied somewhat, the confidence interval overlapped for each model indicating non-significant superiority for the random forest model (AUC = 0.739, 95% CI 0.708-0.769), followed closely by support vector machines (AUC = 0.735, 95% CI 0.707-0.764). PLOS ONEPLOS ONE | https://doi.org/10.1371/journal.pone.

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Associations between adult attention‐deficit/hyperactivity disorder (ADHD) symptoms and dietary habits have not been well established and the underlying mechanisms remain unclear. We explored these associations using a Swedish population‐based twin study with 17,999 individuals aged 20–47 years. We estimated correlations between inattention and hyperactivity/impulsivity with dietary habits and fitted twin models to determine the genetic and environmental contributions. Dietary habits were defined as (a) consumption of food groups, (b) consumption of food items rich in particular macronutrients, and (c) healthy and unhealthy dietary patterns. At the phenotypic level, inattention was positively correlated with seafood, high‐fat, high‐sugar, high‐protein food consumptions, and unhealthy dietary pattern, with correlation coefficients ranging from 0.03 (95%CI: 0.01, 0.05) to 0.13 (95% CI: 0.11, 0.15). Inattention was negatively correlated with fruits, vegetables consumptions and healthy dietary pattern, with correlation coefficients ranging from −0.06 (95%CI: −0.08, −0.04) to −0.07 (95%CI: −0.09, −0.05). Hyperactivity/impulsivity and dietary habits showed similar but weaker patterns compared to inattention. All associations remained stable across age, sex and socioeconomic status. Nonshared environmental effects contributed substantially to the correlations of inattention (56–60%) and hyperactivity/impulsivity (63–80%) with dietary habits. The highest and lowest genetic correlations were between inattention and high‐sugar food (rA = .16, 95% CI: 0.07, 0.25), and between hyperactivity/impulsivity and unhealthy dietary pattern (rA = .05, 95% CI: −0.05, 0.14), respectively. We found phenotypic and etiological overlap between ADHD and dietary habits, although these associations were weak. Our findings contribute to a better understanding of common etiological pathways between ADHD symptoms and various dietary habits.

Internalising symptoms in childhood and adolescence are as heritable as adult depression and anxiety, yet little is known of their molecular basis. This genome-wide association meta-analysis of internalising symptoms included repeated observations from 64,641 individuals, aged between 3 and 18. The N-weighted meta-analysis of overall internalising symptoms (INToverall) detected no genome-wide significant hits and showed low SNP heritability (1.66%, 95% confidence intervals 0.84-2.48%, Neffective=132,260). Stratified analyses showed rater-based heterogeneity in genetic effects, with self-reported internalising symptoms showing the highest heritability (5.63%, 95% confidence intervals 3.08-8.18%). Additive genetic effects on internalising symptoms appeared stable over age, with overlapping estimates of SNP heritability from early-childhood to adolescence. Gene-based analyses showed significant associations with three genes: WNT3 (p=1.13×10-06), CCL26 (p=1.88×10-06), and CENPO (p=2.54×10-06). Of these, WNT3 was previously associated with neuroticism, with which INToverall also shared a strong genetic correlation (rg=0.76). Genetic correlations were also observed with adult anxiety, depression, and the wellbeing spectrum (|rg|> 0.70), as well as with insomnia, loneliness, attention-deficit hyperactivity disorder, autism, and childhood aggression (range |rg|=0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa. Overall, childhood and adolescent internalising symptoms share substantial genetic vulnerabilities with adult internalising disorders and other childhood psychiatric traits, which could explain both the persistence of internalising symptoms over time, and the high comorbidity amongst childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success.

Background and ObjectiveThe prevalence of dementia is increasing, while new opportunities for diagnosing, treating and possibly preventing Alzheimer's disease and other dementia disorders are placing focus on the need for accurate estimates of costs in dementia. Considerable methodological heterogeneity creates challenges for synthesising the existing literature. This study aimed to estimate the costs for persons with dementia in Europe, disaggregated into cost components and informative patient subgroups. Methods We conducted an updated literature review searching PubMed, Embase and Web of Science for studies published from 2008 to July 2021 reporting empirically based cost estimates for persons with dementia in European countries. We excluded highly selective or otherwise biased reports, and used a random-effects meta-analysis to produce estimates of mean costs of care across five European regions. Results Based on 113 studies from 17 European countries, the estimated mean costs for all patients by region were highest in the British Isles (73,712 EUR), followed by the Nordics (43,767 EUR), Southern (35,866 EUR), Western (38,249 EUR), and Eastern Europe and Baltics (7938 EUR). Costs increased with disease severity, and the distribution of costs over informal and formal care followed a North-South gradient with Southern Europe being most reliant on informal care. Conclusions To our knowledge, this study represents the most extensive meta-analysis of the cost for persons with dementia in Europe to date. Though there is considerable heterogeneity across studies, much of this is explained by identifiable factors. Further standardisation of methodology for capturing resource utilisation data may further improve comparability of future studies. The cost estimates presented here may be of value for cost-of-illness studies and economic evaluations of novel diagnostic technologies and therapies for Alzheimer's disease.