Saliva MicroRNA Differentiates Children With Autism From Peers With Typical and Atypical Development

Abstract

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 = 187), TD (n = 125), and DD (n = 69) groups in the training set (n = 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 = 37; TD, n = 8; DD, n = 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 = 0.725) and validation (area under the curve = 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://ichgcp.net/clinical-trials-registry/NCT02832557" title="See in ClinicalTrials.gov">NCT02832557.

Keywords: autism; biomarker; diagnosis; microRNA; saliva.

Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1.. Salivary microRNAs (miRNAs) are Differentially Expressed Across Groups

Note: The 14 miRNAs with differential expression (false discovery rate [FDR]

Figure 2.. Salivary microRNA (miRNA) Profiles Separate Children with Autism Spectrum Disorder (ASD)

Note: (A) A partial least squares discriminant analysis (PLS-DA) was used to map all 381 children in three-dimensional space based on expression of the 527 salivary miRNAs. The PLS-DA demonstrated nearly complete separation of children with ASD; red dots; n=187) from children with typical development ([TD]; blue dots; n=125) while accounting for 14.1% of the variance. There was incomplete spatial separation between children with ASD and children with non-autism developmental delay ([DD]; green dots; n=69). (B) Variable importance in projection (VIP) scores were determined for the 527 individual miRNAs, and the 20 miRNAs with VIP ≥ 2.0 are shown. Color scales demonstrate relative projection importance across ASD, TD, and DD groups. The miRNAs denoted with asterisks represent those identified in previous miRNA studies involving human participants.

Figure 3.. Salivary microRNA (miRNA) Identify Autism Spectrum Disorder (ASD) Status

Note: A logistic regression analysis explored the ability of 28 miRNAs for identifying ASD status, while controlling for medical/demographic covariates. A panel of 4 miRNAs (miR-28–3p, miR-148a-5p, miR-151a-3p, miR-125b-2–3p), that controlled for sex, disordered sleep, attention-deficit/hyperactivity disorder (ADHD), family history of ASD, gastrointestinal disturbance, and chronic medical conditions demonstrated an area under the curve (AUC) of 0.725 (95% CI: 0.650–0.785) in the training set (N=381) using a 100-fold cross validation (CV) approach (blue line). This panel maintained an AUC of 0.694 in the naïve test set (N=62), identifying 33/37 children with ASD and 8/25 peers without ASD. Equation: logit(P) = log(P / (1 − P)) = −0.085 + (10199.182 X Sleep Disorder/miR-28–3p) + (0.014 X Medication/miR-28–3p) + (10199.207 X Family Hx ASD/miR-151a-3p) + (0.042 X GI Disturbance/miR-28–3p) − (10199.229 X Sleep Disorder/miR-151a-3p) (0.029 Sleep Disorder/miR-148a-5p) − (10199.233 X Family Hx ASD/miR-28–3p) − (0.045 X Sleep Disorder/miR-125b-2–3p) + (0.021 X ADHD/miR-28–3p) − (0.058 X Sex/miR-28–3p) − (0.012 X Pregnancy Complications/miR-28–3p) − (0.024 X Any Med Condition/miR-28–3p).