# Identifying Early Markers of Autism Spectrum Disorder Based on Patterns of Medical Symptoms and Healthcare Service

> **NIH NIH R21** · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · 2021 · $197,900

## Abstract

ABSTRACT
 Autism Spectrum Disorder (ASD) is a complex brain disorder marked by difficulties in verbal and non-
verbal social interactions and patterns of repetitive behaviors. Of the approximately 4 million babies born in the
U.S. each year, about 60,000 will be diagnosed with ASD, or about 1 in 59 children based on the recent CDC
estimate. Interventions delivered as early as 12 months of age have been shown to be effective, and early
diagnosis is critical to the success of ASD interventions. However, there has been little progress on identifying
children at high risk for ASD at an early age. Although universal screening could improve the early detection of
ASD, various barriers have kept it from being widely adopted.
 Decades of in-depth research have not only identified behavioral ASD markers but have also shed light
on many other risk factors, such as genetic variants, family and siblings’ medical history, brain abnormalities,
low birth weight, and paternal and maternal ages at childbirth. In addition, there is inconclusive evidence that
some medical conditions, such as otitis media, infections, epilepsy, gastrointestinal problems, birth
complications, and delay in developmental and physiological milestones, may be associated with ASD, but
may manifest well before the onset of hallmark behavioral symptoms of ASD. Despite the fact that individually
these medical symptoms may not be sensitive enough to be used as a viable marker for ASD diagnosis,
combined together, they hold the promise of robustly determining children’s risks for ASD well before any
existing ASD screening tool is currently capable of. The elimination of delayed diagnosis would allow for
optimization of outcomes through early intervention. To the best of our knowledge, there has been little
research to harvest this accumulated knowledge. By leveraging a large, national, longitudinal, private
insurance medical claims database (MarketScan®) and Medicaid claims database (Medicaid Analytic eXtract
or MAX), we will comprehensively investigate the collective role of certain medical symptoms and healthcare
service use patterns as early markers for predicting ASD risk.
 If successful, this study will demonstrate a novel way of improving ASD risk prediction, upon which we
can construct a medical claims-based ASD surveillance system. Working in the background, such a system
can sift through an extensive volume of children’s electronic medical claims records, looking for patterns
indicating potential risk and identifying children for further in-person evaluations when their ASD risk has
crossed a critical threshold. This would ultimately advance ASD early detection and thus ultimately improve the
impact of early intervention therapies.

## Key facts

- **NIH application ID:** 10111571
- **Project number:** 5R21MH119480-02
- **Recipient organization:** PENNSYLVANIA STATE UNIV HERSHEY MED CTR
- **Principal Investigator:** Guodong Liu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $197,900
- **Award type:** 5
- **Project period:** 2020-03-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10111571

## Citation

> US National Institutes of Health, RePORTER application 10111571, Identifying Early Markers of Autism Spectrum Disorder Based on Patterns of Medical Symptoms and Healthcare Service (5R21MH119480-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10111571. Licensed CC0.

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