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

Steven D. Hicks; Randall L. Carpenter; Kayla E. Wagner; Rachel Pauley; Mark Barros; Cheryl Tierney-Aves; Sarah Barns; Cindy Dowd Greene; Frank A. Middleton

doi:10.1016/j.jaac.2019.03.017

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

Steven D. Hicks^*, Randall L. Carpenter, Kayla E. Wagner, Rachel Pauley, Mark Barros, Cheryl Tierney-Aves, Sarah Barns, Cindy Dowd Greene, Frank A. Middleton

^*Corresponding author for this work

Pediatrics

Research output: Contribution to journal › Article › peer-review

62 Scopus citations

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://clinicaltrials.gov/; NCT02832557.

Original language	English (US)
Pages (from-to)	296-308
Number of pages	13
Journal	Journal of the American Academy of Child and Adolescent Psychiatry
Volume	59
Issue number	2
DOIs	https://doi.org/10.1016/j.jaac.2019.03.017
State	Published - Feb 2020

Funding

This study was funded by a research agreement with Quadrant Biosciences Inc. (QB; formerly Motion Intelligence), the SUMU, the Penn State College of Medicine, the Kirson-Kolodner-Fedder Charitable Foundation, and the National Institutes of Health (R41MH111347). The authors thank Jessica Beiler, MPH (Penn State University; PSU) and Richard Uhlig, BS (QB), for assistance with study design, and Jeanette Ramer, MD (PSU), and Carroll Grant, MD (SUMU), for assistance with participant identification. They acknowledge Eric Chin, MD (PSU), Andy Tarasiuk, BS (PSU), Molly Carney, BS (PSU), Falisha Gillman, MD (PSU), Julie Vallati, RN (PSU), Nicole Verdiglione, RN (PSU), Maria Chroneos, BS (PSU), Carrol Grant, PhD (SUMU), Thomas Welch, MD (SUMU), Angela Savage, BS (SUMU), and Parisa Afshari, MD, PhD (SUMU), for assistance with participant recruitment and sample collection. They thank Dongliang Wang, PhD (SUMU) and Jeremy Williams, MS (QB) for guidance with data processing and statistical analysis. Disclosure: Dr. Hicks is a co-inventor of a patent using saliva RNA to identify autism spectrum disorder, which is licensed to QB through PSU and SUMU. He has served as a paid consultant for QB and has held options for QB shares. He has received grant funding from the Gerber Foundation. Dr. Carpenter is a paid employee of QB and holds options for QB shares. Dr. Middleton is a co-inventor of a patent using saliva RNA to identify autism spectrum disorder, which is licensed to QB through PSU and SUMU. Ms. Wagner is a paid employee of QB and holds options for QB shares. Ms. Greene is a paid employee of QB and holds options for QB shares. Drs. Pauley, Barros, Tierney-Aves and Ms. Barns have reported no biomedical financial interests or potential conflicts of interest.

Keywords

autism
biomarker
diagnosis
microRNA
saliva

ASJC Scopus subject areas

Developmental and Educational Psychology
Psychiatry and Mental health

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10.1016/j.jaac.2019.03.017

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Hicks, S. D., Carpenter, R. L., Wagner, K. E., Pauley, R., Barros, M., Tierney-Aves, C., Barns, S., Greene, C. D., & Middleton, F. A. (2020). Saliva MicroRNA Differentiates Children With Autism From Peers With Typical and Atypical Development. Journal of the American Academy of Child and Adolescent Psychiatry, 59(2), 296-308. https://doi.org/10.1016/j.jaac.2019.03.017

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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 na{\"i}ve 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://clinicaltrials.gov/; NCT02832557.",

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AU - Carpenter, Randall L.

AU - Wagner, Kayla E.

AU - Pauley, Rachel

AU - Barros, Mark

AU - Tierney-Aves, Cheryl

AU - Barns, Sarah

AU - Greene, Cindy Dowd

AU - Middleton, Frank A.

PY - 2020/2

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N2 - 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://clinicaltrials.gov/; NCT02832557.

AB - 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://clinicaltrials.gov/; NCT02832557.

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

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