![]() For instance, children with ASD are typified by hyperserotonemia ( Chamberlain and Herman, 1990), elevated oxidative stress markers ( Chauhan and Chauhan, 2006), and alterations in immune factors that can lead to neuroinflammation ( Corbett et al., 2006 Goines and Van de Water, 2010 Onore et al., 2012). Addition of a rapid, accurate, objective adjunct measure could improve care for children with ASD.Įfforts to identify ASD biomarkers have yielded much information about the biologic basis of ASD. This wait can delay initiation of critical intervention services at a time of marked brain development. Non-specific screening tools may lead to over-referral, contributing to wait times that exceed 12 months for diagnostic evaluation using the Diagnostic and Statistics Manual (DSM)-5 criteria ( Bisgaier et al., 2011). Preventive Services Task Force determined that insufficient evidence existed to recommend continued ASD screening. Screening for ASD typically relies on the modified checklist for autism in toddlers – revised (MCHAT-R), a parent-based survey with a positive predictive value less than 20% ( Siu et al., 2016). This technology could improve the specificity of referrals for ASD evaluation or provide objective support for ASD diagnoses.Ĭhildren with autism spectrum disorder (ASD) are characterized by social/communication deficits and restricted/repetitive behaviors, but display marked variation at the genetic, phenotypic, and functional levels ( Geschwind, 2008). Notably, the RNA features were implicated in physiologic processes related to ASD (axon guidance, neurotrophic signaling).Ĭonclusion: Salivary poly-omic RNA measurement represents a novel, non-invasive approach that can accurately identify children with ASD. ![]() 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). ![]() 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). The validation set was not used in model development (feature selection or training) but served only to validate empirical accuracy. The training set was used to develop an RNA-based algorithm that distinguished ASD and non-ASD children. Prior to analysis, the sample was randomly divided into a training set (82% of subjects) and an independent validation test set (18% of subjects). Comprehensive human and microbial RNA abundance was measured in the saliva of all participants using unbiased next generation sequencing. Children were either neurotypical ( n = 134) or had a diagnosis of ASD ( n = 238), or non-ASD developmental delay ( n = 84). Methods: This multi-center cross-sectional study included 456 children, ages 19–83 months. 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. 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. Efforts to define biomarkers of ASD have not resulted in an objective, reliable test. 5Department of Pediatrics, State University of New York Upstate Medical University, Syracuse, NY, United Statesīackground: The diagnosis of autism spectrum disorder (ASD) relies on behavioral assessment.4Departments of Psychiatry, Biochemistry and Molecular Biology, State University of New York Upstate Medical University, Syracuse, NY, United States.3Departments of Neuroscience and Physiology, State University of New York Upstate Medical University, Syracuse, NY, United States.2Quadrant Biosciences, Inc., Syracuse, NY, United States.1Department of Pediatrics, Penn State College of Medicine, Hershey, PA, United States.
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