dbGaP Study Accession: phs002549

NIH Institute/Center: NICHD

RADx Data Program: RADx-rad

Release Date: 07/21/2022

DOI: 10.60773/fp1q-f015

Study Description: Children have been disproportionately less impacted by the Corona Virus Disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) compared to adults. However, severe illnesses including Multisystem Inflammatory Syndrome (MIS-C) and respiratory failure have occurred in a small proportion of children with SARS-CoV-2 infection. Nearly 80% of children with MIS-C are critically ill with a 2-4% mortality rate. Currently, there are no modalities to characterize the spectrum of disease severity and predict which child with SARS-CoV-2 exposure will likely develop severe illness including MIS-C. Thus, there is an urgent need to develop a diagnostic modality to distinguish the varying phenotypes of disease and risk stratify disease. The epigenetic changes in microRNA (miRNA) profiles that occur due to an infection can impact disease severity by altering immune response and cytokine regulation which may be detected in body fluids including saliva. The long-term goal of this study was to improve outcomes of children with SARS-CoV-2 by early identification and treatment of those at risk for severe illness. The central hypothesis was that a model that integrates salivary biomarkers with social and clinical determinants of health would predict disease severity in children with SARS-CoV-2 infection. This central hypothesis was pursued through four specific, phased aims. The first two aims from the R61 phase were: 1) Defining and comparing the salivary molecular host response in children with varying phenotypes (severe and non-severe) SARS-CoV-2 infections, and 2) Developing and validating a sensitive and specific model to predict severe SARS-CoV-2 illness in children. The R33 phase pursued the following two aims: 3) Development of a portable, rapid device that quantifies salivary miRNAs with comparable accuracy to predicate technology (qRT-PCR), and 4) Development of an artificial intelligence (AI) assisted cloud and mobile system for early recognition of severe SARS-CoV-2 infection in children. The above aims were pursued using an innovative combination of salivaomics and bioinformatics, analytic techniques of AI and clinical informatics. This research is significant because development of a sensitive model to risk stratify disease is expected to improve outcomes of children with severe SARS-CoV-2 infection via early recognition and timely intervention. The outcome of this study is a better understanding of the epigenetic regulation of host immune response to the viral infection which is expected to lead to personalized therapy in the future. The results have immediately had a positive impact and will lead to the creation of patient profiles based on individual risk factors which can enable early identification of severe disease and appropriate resource allocation during the pandemic.

Updated Date: 01/17/2024

Principal Investigator: Sethuraman, Usha

Has Data Files: No

Study Domain: Artificial Intelligence or Machine Learning; Medical Device or Tool Development; Multisystem Inflammatory Syndrome (MIS); Multisystem Inflammatory Syndrome in Children (MIS-C); Rapid Diagnostic Test (RDT); Social Determinants of Health

Data Collection Method: Molecular Nucleic Acid or PCR Testing Device

Keywords: Bioinformatics; Cytokine Regulation; Disease Severity Risk Stratification; Pediatric COVID-19; Salivaomics; Severe SARS-CoV-2 Infection

Study Design: Longitudinal Cohort

Multi-Center Study: Yes

Study Sites: Central Michigan University; Penn State University; UPMC Children's hospital of Pittsburgh/University of Pittsburgh; Wayne State University

Data Types: Clinical; Electronic Medical Records; Family History; Imaging; Immunological; Individual Phenotype; Questionnaire or Survey; Sequencing; Social

Study Start Date: 01/01/2021

Study End Date: 11/30/2022

Species: Human Data

Estimated Cohort Size: 400

Study Population Focus: Children

Publication URL: https://pubmed.ncbi.nlm.nih.gov/34054330/; https://pubmed.ncbi.nlm.nih.gov/35906312/; https://pubmed.ncbi.nlm.nih.gov/36292760/; https://pubmed.ncbi.nlm.nih.gov/36748919/; https://pubmed.ncbi.nlm.nih.gov/36854096/

Acknowledgement Statement: This study was supported through funding, 4R61HD105610-02, for the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) as part of the RADx-rad program. Principal investigators for this study are Dr. Usha Sethuraman, Dr. Dongxiao Zhu, and Dr. Steve Hicks. Other collaborators on this study are Dr. Srinivasan Suresh, Dr. Nirupama Kannikeswaran, Dr. Kathleen Meert, Dr. Wei Chen, Dr. Anna Ettinger, Dr. John Williams, Dr. Scott Halstead and Dr. Weihua Guan. The collaborating sites of this study are Central Michigan University, Wayne State University, University of Pittsburgh and Penn State University. The dataset originated from patients enrolled at Children's Hospital of Michigan/Central Michigan University and at UPMC Children's Hospital of Pittsburgh. All miRNA and cytokine profiling were performed at Penn State while model development occurred at Wayne State University. Approved users should acknowledge the provision of data access by dbGaP for accession phs002549.v1.p1, and the NIH RADx Data Hub. Approved users should also acknowledge the specific version(s) of the dataset(s) obtained from the NIH RADx Data Hub.

Funding Opportunity Announcement (FOA) Number: RFA-OD-20-023

NIH Grant or Contract Number(s): 4R61HD105610-02

Consent/Data Use Limitations: General Research Use