Seize the data: addressing research challenges among children with autism spectrum disorder using statistical and machine learning techniques

Abstract

Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental condition that is estimated to affect about 1 in 44 children in the United States. While the etiology of ASD continues to be an area of intense investigation, the use of machine learning and multivariate statistical methods hold considerable promise in approaching clinically relevant questions regarding the nature of this condition. These techniques have shown great potential to identify underlying patterns in metabolomic and environmental data that have significance for ASD diagnosis and behavioral severity prediction. Nonetheless, biomarkers are not currently utilized in clinical settings to aid in diagnosis, and the degree to which various cellular/metabolic pathways converge to behavioral symptoms is poorly understood. This thesis seeks to leverage statistical learning and systems biology techniques on datasets derived from children with ASD to achieve three main goals. Firstly, this work analyzes urinary elemental measurements taken from a cohort of children and their mothers to identify possible environmental and physiological differences that could underscore areas pertinent to ASD etiology and mechanisms of action. Next, metabolomic differences observed between children with ASD and their typically developing counterparts are evaluated in blood, urine, and feces to expand the repertoire of potential clinically relevant biomarkers. Reliable biochemical biomarkers can pave the way for less subjectivity in diagnosis and for earlier detection, which can allow for better treatment outcomes and improve access to resources for caregivers. The interplay between prominent potential biomarkers and behavioral/comorbid symptoms are also identified to provide context for the development of targeted intervention and treatment strategies. Finally, the analysis of a microbiota transplant therapy (MTT) study performed on a cohort of children with ASD and gastrointestinal issues is presented. The effectiveness of this treatment in ameliorating both GI and severe behavioral symptoms is presented, along with its environmental and metabolomic implications.