A Deep Learning Algorithm Platform to Predict Autism Diagnosis and Subtypes

A Deep Learning Algorithm Platform to Predict Autism Diagnosis and Subtypes by Integrating Clinical, Cognitive, Imaging, Gut Microbiome, and Metabolome Data

This is the first human study on ASD microbiome with robust methodologies: prospective and sibling designs, metagenomics profiles, establishing an ASD multi-dimensional databank (clinic, behavior, neurocognition, brain imaging, metabolomics, and microbiome) collected using the same methodology and genetic biology simultaneously, and developing a deep learning platform for ASD diagnosis and prevention. With the accomplishment of this project, we anticipate establishing a web application for clinical and academic use. Our findings will further advance the knowledge in the pathogenetic mechanisms of ASD to enhance early detection, diagnosis, and treatment, subsequently contributing to precision medicine.

Study Overview

• Status: Recruiting
• Conditions: Autism Spectrum Disorder
• Intervention / Treatment: Other: ASD diagnosis; Other: Psychiatric diagnosis

Detailed Description

Due to the high prevalence (1% in Taiwan), long-lasting impairment, unclear etiologies, and a lack of effective detection, prevention, and biological treatment, autism spectrum disorder (ASD) has been prioritized for biomarker, mechanism, and treatment research. Recently the gut-brain-axis has been proved, mainly with animal models, to be altered in psychiatric disorders and notably in ASD. With PI Gau's long-term achievement in ASD multi-dimensional research and our preliminary finding of altered gut microbiota in ASD and their unaffected siblings, we propose this 4-year prospective large-scale study with sibling design and multi-dimensional measures (environmental, clinical, cognitive, imaging, gut microbiome, metabolome) to establish a deep learning algorithm platform for predicting ASD and searching potential biomarkers and probiotic treatment for ASD.

Specific Aims:

1. To demonstrate the metagenomics profiles analysis based on the gut microbiome and metabolome of ASD patients, unaffected siblings, and typically developing controls (TDC).
2. To investigate environmental factors such as pregnancy and birth history from the mother's medical records and interviews or national health insurance data, for the microbiome, metagenomics, and brain anatomy and function.
3. To develop a deep learning algorithm platform using the environmental, behavioral/clinical phenotypes, neurocognitive/imaging endophenotypes, and metagenomics profiles to identify microbiota (metagenomics, too) makers and other predictors for ASD diagnosis, subtypes, and level of impairments.
4. To establish a web application based on our deep learning algorithm platform for clinical use to assist medical doctors in diagnosing ASD.

• Study Type: Observational
• Enrollment (Estimated): 420

Contacts and Locations

Study Locations

• Taiwan
  • Taipei, Taiwan
    • Recruiting
    • National Taiwan Univeristy Hospital
    • Contact: Susan Shur-Fen Gau, MD, PhD; Phone Number: 66802 886-2-23123456; Email: gaushufe@ntu.edu.tw

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 4 years to 25 years (Child, Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The sample includes 240 ASD (aged 4 to 25 years), 60~100 (estimated average 80) unaffected siblings (US), and 120 TDC. 240 ASD and 80 US, aged 4-25 will be recruited from the ASD follow-up cohort established by PI Gau's NHRI project or the Department of Psychiatry of all the branches of NTUH.

Description

Inclusion Criteria:

• ASD participants are (1) they have a clinical diagnosis of ASD defined by the DSM-5 criteria,1 made by board-certificated child psychiatrists and confirmed by the ADI-R/ADOS; (2) their ages range from 4 to 25; (3) both parents are Han Chinese; (4) they and their parents cooperate with all the assessments and stool and blood collection.

Inclusion Criteria for US and TDC are (1) they do not reach the clinical diagnosis of ASD according to DSM-5 diagnostic criteria and the same criteria as described in the (2), (3), (4) and of Inclusion Criteria for ASD participants.

Exclusion Criteria:

• (1) comorbidity with DSM-5 diagnoses of schizophrenia, schizoaffective disorder, delusional disorder, other psychotic disorders, organic psychosis, schizotypal personality disorder, bipolar disorder, depression, severe anxiety disorders or substance use; (2) comorbidity with neurological or systemic disorders; and (3) having a first degree relative who may have ASD based on family history method assessment (the TDC group).

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
ASD group; 240 ASD patients (aged 4-25 years)	Other: ASD diagnosis; Autism Diagnostic Interview-revised (ADI-R) and Autism Diagnostic Observation Scale (ADOS); Other: Psychiatric diagnosis; Kiddie Schedule for Affective Disorders & Schizophrenia (K-SADS) for DSM-5
Unaffected siblings of ASD; 60-100 unaffected siblings of ASD probands	Other: Psychiatric diagnosis; Kiddie Schedule for Affective Disorders & Schizophrenia (K-SADS) for DSM-5
TD group; 120 age-, and sex matched TDC from the same geographic areas of the ASD group via referral by teachers, or advertisement at college or community.	Other: Psychiatric diagnosis; Kiddie Schedule for Affective Disorders & Schizophrenia (K-SADS) for DSM-5

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Neuropsychological functions: Cambridge Neuropsychological Test Automated Batteries(CANTAB)	The 4 main cognitive components of CANTAB: Visual Memory, Attention, Working and Planning Memory (Executive Functions), and Decision Making	1.5 hours
Autism diagnostic interview (ADI-R)	Including reciprocal social interaction, communication, and repetitive behaviors and stereotyped patterns, for children with a mental age from about 18 months into adulthood	4 hours
Neuropsychological functions: Continuous Performance Test(CPT)	The 4 dimensions of CCPT: focused attention, hyperactivity/impulsivity, sustained attention, and vigilance	15 minutes

Collaborators and Investigators

• Sponsor: National Taiwan University Hospital

Study record dates

Study Major Dates

• Study Start (Actual): May 1, 2020
• Primary Completion (Estimated): April 1, 2024
• Study Completion (Estimated): April 1, 2024

Study Registration Dates

• First Submitted: April 29, 2021
• First Submitted That Met QC Criteria: May 4, 2021
• First Posted (Actual): May 5, 2021

Study Record Updates

• Last Update Posted (Actual): October 17, 2023
• Last Update Submitted That Met QC Criteria: October 16, 2023
• Last Verified: October 1, 2023

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Mental Disorders; Neurodevelopmental Disorders; Child Development Disorders, Pervasive; Autistic Disorder; Autism Spectrum Disorder
• Other Study ID Numbers: 202002086RIND

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No