Objective: To clarify the role of oxidative stress and antioxidant activity in ADHD. Method: We examined the association of ADHD and oxidative stress by applying random effects meta-analysis to studies of oxidative stress and antioxidant status in medication naive patients with ADHD and controls. Results: Six studies of a total of 231 ADHD patients and 207 controls met our selection criteria. The association between ADHD and antioxidant status was not significant. We found a significant association between ADHD and oxidative stress that could not be accounted for by publication bias. The significant association lost significance after correcting for intrastudy clustering. No one observation accounted for the positive result. Conclusion: These results are preliminary given the small number of studies. They suggest that patients with ADHD have normal levels of antioxidant production, but that their response to oxidative stress is insufficient, leading to oxidative damage.

ADHD and autism spectrum disorder are common psychiatric comorbidities to each another. In addition, there is behavioral, biological and neuropsychological overlap between the two disorders. There are also several important differences between autism spectrum disorder and ADHD. Treatment strategies for the comorbid condition will also be reviewed. Future areas of research and clinical need will be discussed.

Background: Suicide is a major public health concern globally. Accurately predicting suicidal behavior remains challenging. This study aimed to use machine learning approaches to examine the potential of the Swedish national registry data for prediction of suicidal behavior. Methods and findings: The study sample consisted of 541,300 inpatient and outpatient visits by 126,205 Sweden-born patients (54% female and 46% male) aged 18 to 39 (mean age at the visit: 27.3) years to psychiatric specialty care in Sweden between January 1, 2011 and December 31, 2012. The most common psychiatric diagnoses at the visit were anxiety disorders (20.0%), major depressive disorder (16.9%), and substance use disorders (13.6%). A total of 425 candidate predictors covering demographic characteristics, socioeconomic status (SES), electronic medical records, criminality, as well as family history of disease and crime were extracted from the Swedish registry data. The sample was randomly split into an 80% training set containing 433,024 visits and a 20% test set containing 108,276 visits. Models were trained separately for suicide attempt/death within 90 and 30 days following a visit using multiple machine learning algorithms. Model discrimination and calibration were both evaluated. Among all eligible visits, 3.5% (18,682) were followed by a suicide attempt/death within 90 days and 1.7% (9,099) within 30 days. The final models were based on ensemble learning that combined predictions from elastic net penalized logistic regression, random forest, gradient boosting, and a neural network. The area under the receiver operating characteristic (ROC) curves (AUCs) on the test set were 0.88 (95% confidence interval [CI] = 0.87–0.89) and 0.89 (95% CI = 0.88–0.90) for the outcome within 90 days and 30 days, respectively, both being significantly better than chance (i.e., AUC = 0.50) (p < 0.01). Sensitivity, specificity, and predictive values were reported at different risk thresholds. A limitation of our study is that our models have not yet been externally validated, and thus, the generalizability of the models to other populations remains unknown. Conclusions: By combining the ensemble method of multiple machine learning algorithms and high-quality data solely from the Swedish registers, we developed prognostic models to predict short-term suicide attempt/death with good discrimination and calibration. Whether novel predictors can improve predictive performance requires further investigation.

Human genome-wide association studies (GWAS), transcriptome analyses of animal models, and candidate gene studies have advanced our understanding of the genetic architecture of aggressive behaviors. However, each of these methods presents unique limitations. To generate a more confident and comprehensive view of the complex genetics underlying aggression, we undertook an integrated, cross-species approach. We focused on human and rodent models to derive eight gene lists from three main categories of genetic evidence: two sets of genes identified in GWAS studies, four sets implicated by transcriptome-wide studies of rodent models, and two sets of genes with causal evidence from online Mendelian inheritance in man (OMIM) and knockout (KO) mice reports. These gene sets were evaluated for overlap and pathway enrichment to extract their similarities and differences. We identified enriched common pathways such as the G-protein coupled receptor (GPCR) signaling pathway, axon guidance, reelin signaling in neurons, and ERK/MAPK signaling. Also, individual genes were ranked based on their cumulative weights to quantify their importance as risk factors for aggressive behavior, which resulted in 40 top-ranked and highly interconnected genes. The results of our cross-species and integrated approach provide insights into the genetic etiology of aggression.

The spontaneously hypertensive rat (SHR) has been widely used as a model for studies of hypertension and attention deficit/hyperactivity disorder. The inbred Wistar-Kyoto (WKY) rat, derived from the same ancestral outbred Wistar rat as the SHR, are normotensive and have been used as the closest genetic control for the SHR, although the WKY has also been used as a model for depression. Notably, however, substantial behavioral and genetic differences among the WKY substrains, usually from the different vendors and breeders, have been observed. These differences have often been overlooked in prior studies, leading to inconsistent and even contradictory findings. The complicated breeding history of the SHR and WKY rats and the lack of a comprehensive understanding of the genetic background of different commercial substrains make the selection of control rats a daunting task, even for researchers who are mindful of their genetic heterogeneity. In this study, we examined the genetic relationship of 16 commonly used WKY and SHR rat substrains using genome-wide SNP genotyping data. Our results confirmed a large genetic divergence and complex relationships among the SHR and WKY substrains. This understanding, although incomplete without the genome sequence, provides useful guidance in selecting substrains and helps to interpret previous reports when the source of the animals was known. Moreover, we found two closely related, yet distinct WKY substrains that may provide novel opportunities in modeling psychiatric disorders.

According to DSM-IV there are three subtypes of Attention-Deficit/Hyperactivity Disorder, namely: ADHD predominantly inattentive type (ADHD-PI), ADHD predominantly Hyperactive-Impulsive Type (ADHD-HI), and ADHD combined type (ADHD-C). These subtypes may represent distinct neurobehavioral disorders of childhood onset with separate etiologies. The diagnosis of ADHD is behaviorally based; therefore, investigations into its possible etiologies should be based in behavior. Animal models of ADHD demonstrate construct validity when they accurately reproduce elements of the etiology, biochemistry, symptoms, and treatment of the disorder. Spontaneously hypertensive rats (SHR) fulfill many of the validation criteria and compare well with clinical cases of ADHD-C. The present study describes a novel rat model of the predominantly inattentive subtype (ADHD-PI).