Gut problems in autistic children and young adults: finding who is at risk and who responds to treatment
Characterizing gastrointestinal disorder trajectories for autistic sub-groups: Machine learning prediction of risk profiles and response to treatment
This project uses interviews, hospital records, and machine learning to find patterns of gut problems and which autistic children, teens, and young adults respond to common treatments.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Southern California NIH-funded |
| Lab location | 1 site (Los Angeles, UNITED STATES) |
| Project ID | NIH-11318931 on NIH RePORTER |
What this research studies
Researchers will interview 25 autistic adults and 25 caregivers to collect personal stories about gut symptoms and their effects. They will also analyze electronic health records from Children’s Hospital Los Angeles for about 7,478 autistic patients ages 1–25 to map symptom histories, treatments, and outcomes. Machine learning models will look for subgroups at higher risk for particular gastrointestinal problems and for factors linked to better or worse treatment response. Autistic people are involved in the work to help make sure the findings match real-life experiences.
Who could benefit from this research
Good fit: Ideal participants are autistic children, teens, or young adults (about ages 1–25) who receive care at Children’s Hospital Los Angeles, plus autistic adults or caregivers willing to share detailed GI experiences.
Not a fit: People without GI concerns, those outside the study age range, or individuals not seen at CHLA may not directly benefit from this project.
Why it matters
Potential benefit: Could help clinicians spot which autistic people are more likely to develop specific GI problems and choose treatments that work better for each subgroup.
How similar studies have performed: Most prior work relied on parent reports and single-time snapshots, so combining interviews, large hospital records, and machine learning is relatively new and less tested.
Where this research is happening
Los Angeles, UNITED STATES
- University of Southern California — Los Angeles, United States (Active)
Researchers
- Principal investigator: Angell, Amber M. — University of Southern California
- Study coordinator: Angell, Amber M.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.