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A Deep Learning Algorithm Platform to Predict Autism Diagnosis and Subtypes

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ClinicalTrials.gov Identifier: NCT04873674
Recruitment Status : Recruiting
First Posted : May 5, 2021
Last Update Posted : May 5, 2021
Sponsor:
Information provided by (Responsible Party):
National Taiwan University Hospital

Study Description
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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.

Condition or disease Intervention/treatment
Autism Spectrum Disorder 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 Design
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Study Type : Observational
Estimated Enrollment : 420 participants
Observational Model: Case-Control
Time Perspective: Cross-Sectional
Official Title: A Deep Learning Algorithm Platform to Predict Autism Diagnosis and Subtypes by Integrating Clinical, Cognitive, Imaging, Gut Microbiome, and Metabolome Data
Actual Study Start Date : May 1, 2020
Estimated Primary Completion Date : April 2024
Estimated Study Completion Date : April 2024

Resource links provided by the National Library of Medicine


Groups and Cohorts
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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


Outcome Measures
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Primary Outcome Measures :
  1. Autism diagnostic interview (ADI-R) [ Time Frame: 4 hours ]
    Including reciprocal social interaction, communication, and repetitive behaviors and stereotyped patterns, for children with a mental age from about 18 months into adulthood

  2. Neuropsychological functions: Continuous Performance Test(CPT) [ Time Frame: 15 minutes ]
    The 4 dimensions of CCPT: focused attention, hyperactivity/impulsivity, sustained attention, and vigilance

  3. Neuropsychological functions: Cambridge Neuropsychological Test Automated Batteries(CANTAB) [ Time Frame: 1.5 hours ]
    The 4 main cognitive components of CANTAB: Visual Memory, Attention, Working and Planning Memory (Executive Functions), and Decision Making


Biospecimen Retention:   Samples With DNA
metabolite (blood) and intestinal microbial (stool)

Eligibility Criteria
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Ages Eligible for Study:   4 Years to 25 Years   (Child, Adult)
Sexes Eligible for Study:   All
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.
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.

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).
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04873674


Locations
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Taiwan
National Taiwan Univeristy Hospital Recruiting
Taipei, Taiwan
Contact: Susan Shur-Fen Gau, MD, PhD    886-2-23123456 ext 66802    gaushufe@ntu.edu.tw   
Sponsors and Collaborators
National Taiwan University Hospital
More Information
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Responsible Party: National Taiwan University Hospital
ClinicalTrials.gov Identifier: NCT04873674    
Other Study ID Numbers: 202002086RIND
First Posted: May 5, 2021    Key Record Dates
Last Update Posted: May 5, 2021
Last Verified: April 2021

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Autistic Disorder
Autism Spectrum Disorder
Child Development Disorders, Pervasive
Neurodevelopmental Disorders
Mental Disorders