A Deep Learning Algorithm Platform to Predict Autism Diagnosis and Subtypes

NCT ID: NCT04873674

Last Updated: 2023-10-17

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 420 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2020-05-01
• Study Completion Date: 2024-04-30

Brief Summary

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.

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.

Conditions

Autism Spectrum Disorder

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: CROSS_SECTIONAL

Study Groups

ASD group

240 ASD patients (aged 4-25 years)

ASD diagnosis

Intervention Type: OTHER

Autism Diagnostic Interview-revised (ADI-R) and Autism Diagnostic Observation Scale (ADOS)

Psychiatric diagnosis

Intervention Type: OTHER

Kiddie Schedule for Affective Disorders \& Schizophrenia (K-SADS) for DSM-5

Unaffected siblings of ASD

60-100 unaffected siblings of ASD probands

Psychiatric diagnosis

Intervention Type: OTHER

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.

Psychiatric diagnosis

Intervention Type: OTHER

Kiddie Schedule for Affective Disorders \& Schizophrenia (K-SADS) for DSM-5

Interventions

ASD diagnosis

Autism Diagnostic Interview-revised (ADI-R) and Autism Diagnostic Observation Scale (ADOS)

Intervention Type: OTHER

Psychiatric diagnosis

Kiddie Schedule for Affective Disorders \& Schizophrenia (K-SADS) for DSM-5

Intervention Type: OTHER

Eligibility Criteria

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.

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).

• Minimum Eligible Age: 4 Years
• Maximum Eligible Age: 25 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Taiwan University Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

National Taiwan Univeristy Hospital

Taipei, , Taiwan

Site Status: RECRUITING

Countries

Taiwan

Facility Contacts

Susan Shur-Fen Gau, MD, PhD

Role: primary

gaushufe@ntu.edu.tw

886-2-23123456 ext. 66802

Other Identifiers

202002086RIND

Identifier Type: -

Identifier Source: org_study_id