Abstract:
Developmental disorders like autism spectrum disorder (ASD) can result from differences in children's brains.
Neurodevelopmental problems are exacerbated by a confluence of genetic, environmental, and prenatal risk factors associated with
ASD. Repetitive behaviors and deficiencies in social communication are among the early indicators in children. Although gestational
risk factors are not the cause of ASD, they can impact how children interact with one another. On the other hand, these risk factors
might also favorably influence the development of ASD, and women who are pregnant can help with interventions. Later in life, the
development of autism is linked to changes in lipid levels at birth. Dyslipidemia, characterized by abnormal cholesterol and
triglyceride levels, is more common in individuals with ASD than in their healthy siblings or unrelated controls. However, the
specific predictive value of blood lipid profiles and the key markers for dyslipidemia associated with ASD remain unclear. This
paper explores the influence of infant lipid levels on the development of dyslipidemia associated with ASD, considering gestational
risk factors for mothers. A machine learning model is constructed using combined parental and childhood lipid levels to predict
ASD. The model is then validated using independent cohorts and tested against lipid profiles from infancy. Various statistical
approaches designed for biomarker discovery in Electronic Health Records (EHR) data are applied to achieve these objectives.
