Attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) commonly co-occur. With the DSM-5, clinicians are permitted to make an ASD diagnosis in the context of ADHD. In earlier versions of the DSM, this was not acceptable. Both ASD and ADHD are reported to have had substantial increases in prevalence within the past 10 years. As a function of both the increased prevalence of both disorders as well as the ability to make an ASD diagnosis in ADHD, there has been a significant amount of research focusing on the comorbidity between ADHD and ASD in the past few years. Here, we provide an update on the biological, cognitive and behavioral overlap/distinctiveness between the two neurodevelopmental disorders with a focus on data published in the last four years. Treatment strategies for the comorbid condition as well as future areas of research and clinical need are discussed.

Autism spectrum disorder (ASD) is associated with several oropharyngeal abnormalities, including buccal sensory sensitivity, taste and texture aversions, speech apraxia, and salivary transcriptome alterations. Furthermore, the oropharynx represents the sole entry point to the gastrointestinal (GI) tract. GI disturbances and alterations in the GI microbiome are established features of ASD, and may impact behavior through the "microbial-gut-brain axis." Most studies of the ASD microbiome have used fecal samples. Here, we identified changes in the salivary microbiome of children aged 2-6 years across three developmental profiles: ASD (n = 180), nonautistic developmental delay (DD; n = 60), and typically developing (TD; n = 106) children. After RNA extraction and shotgun sequencing, actively transcribing taxa were quantified and tested for differences between groups and within ASD endophenotypes. A total of 12 taxa were altered between the developmental groups and 28 taxa were identified that distinguished ASD patients with and without GI disturbance, providing further evidence for the role of the gut-brain axis in ASD. Group classification accuracy was visualized with receiver operating characteristic curves and validated using a 50/50 hold-out procedure. Five microbial ratios distinguished ASD from TD participants (79.5% accuracy), three distinguished ASD from DD (76.5%), and three distinguished ASD children with/without GI disturbance (85.7%). Taxonomic pathways were assessed using the Kyoto Encyclopedia of Genes and Genomes microbial database and compared with one-way analysis of variance, revealing significant differences within energy metabolism and lysine degradation. Together, these results indicate that GI microbiome disruption in ASD extends to the oropharynx, and suggests oral microbiome profiling as a potential tool to evaluate ASD status. Autism Res 2018, 11: 1286-1299. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Previous research suggests that the bacteria living in the human gut may influence autistic behavior. This study examined genetic activity of microbes living in the mouth of over 300 children. The microbes with differences in children with autism were involved in energy processing and showed potential for identifying autism status.

Objective: Clinical diagnosis of autism spectrum disorder (ASD) relies on time-consuming subjective assessments. The primary purpose of this study was to investigate the utility of salivary microRNAs for differentiating children with ASD from peers with typical development (TD) and non-autism developmental delay (DD). The secondary purpose was to explore microRNA patterns among ASD phenotypes. Method: This multicenter, prospective, case-control study enrolled 443 children (2-6 years old). ASD diagnoses were based on DSM-5 criteria. Children with ASD or DD were assessed with the Autism Diagnostic Observation Schedule II and Vineland Adaptive Behavior Scales II. MicroRNAs were measured with high-throughput sequencing. Differential expression of microRNAs was compared among the ASD (n ¼ 187), TD (n ¼ 125), and DD (n ¼ 69) groups in the training set (n ¼ 381). Multivariate logistic regression defined a panel of microRNAs that differentiated children with ASD and those without ASD. The algorithm was tested in a prospectively collected naïve set of 62 samples (ASD, n ¼ 37; TD, n ¼ 8; DD, n ¼ 17). Relations between microRNA levels and ASD phenotypes were explored. Result: Fourteen microRNAs displayed differential expression (false discovery rate < 0.05) among ASD, TD, and DD groups. A panel of 4 microRNAs (controlling for medical/demographic covariates) best differentiated children with ASD from children without ASD in training (area under the curve ¼ 0.725) and validation (area under the curve ¼ 0.694) sets. Eight microRNAs were associated (R > 0.25, false discovery rate < 0.05) with social affect, and 10 microRNAs were associated with restricted/repetitive behavior. Conclusion: Salivary microRNAs are "altered" in children with ASD and associated with levels of ASD behaviors. Salivary microRNA collection is noninvasive, identifying ASD-status with moderate accuracy. A multi-"omic" approach using additional RNA families could improve accuracy, leading to clinical application. Clinical trial registration information: A Salivary miRNA Diagnostic Test for Autism; https://clinicaltrials.gov/; NCT02832557.

Background: The diagnosis of autism spectrum disorder (ASD) relies on behavioral assessment. Efforts to define biomarkers of ASD have not resulted in an objective, reliable test. Studies of RNA levels in ASD have demonstrated potential utility, but have been limited by a focus on single RNA types, small sample sizes, and lack of developmental delay controls. We hypothesized that a saliva-based poly-“omic” RNA panel could objectively distinguish children with ASD from their neurotypical peers and children with non-ASD developmental delay.Methods: This multi-center cross-sectional study included 456 children, ages 19–83 months. Children were either neurotypical (n = 134) or had a diagnosis of ASD (n = 238), or non-ASD developmental delay (n = 84). Comprehensive human and microbial RNA abundance was measured in the saliva of all participants using unbiased next generation sequencing. Prior to analysis, the sample was randomly divided into a training set (82% of subjects) and an independent validation test set (18% of subjects). The training set was used to develop an RNA-based algorithm that distinguished ASD and non-ASD children. The validation set was not used in model development (feature selection or training) but served only to validate empirical accuracy.Results: In the training set (n = 372; mean age 51 months; 75% male; 51% ASD), a set of 32 RNA features (controlled for demographic and medical characteristics), identified ASD status with a cross-validated area under the curve (AUC) of 0.87 (95% CI: 0.86–0.88). In the completely separate validation test set (n = 84; mean age 50 months; 85% male; 60% ASD), the algorithm maintained an AUC of 0.88 (82% sensitivity and 88% specificity). Notably, the RNA features were implicated in physiologic processes related to ASD (axon guidance, neurotrophic signaling).Conclusion: Salivary poly-omic RNA measurement represents a novel, non-invasive approach that can accurately identify children with ASD. This technology could improve the specificity of referrals for ASD evaluation or provide objective support for ASD diagnoses.

Background The primary objectives of the current prospective longitudinal study were to (a) describe social functioning outcomes and (b) identify childhood predictors of social functioning in young adults with 22q11.2 Deletion Syndrome (22q11.2DS). Method Childhood predictors of young adult social functioning were examined. Family environment and parental stress in adolescence were investigated as potential mediators between childhood variables and adult social functioning. Results Parent rated childhood internalizing symptoms significantly predicted young adult social functioning in 22q11.2DS, even after controlling for concurrent positive symptoms of psychosis, and problem behaviors contributing to parenting stress in adolescence partially mediated this relationship. Conclusions These findings highlight child internalizing symptoms and adolescent problem behaviors as potential targets for social functioning interventions in 22q11.2DS.