Deciphering the Autism Spectrum Disorder Beyond Genomics

NCT ID: NCT03676673

Last Updated: 2022-09-26

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 200 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2018-08-01
• Study Completion Date: 2021-12-31

Brief Summary

The investigators propose to study the molecular etiology of autism spectrum disorder(ASD) from a genomic, metabolomics and network biology perspective by combining data of gene expression, sequence variations and metabolism conditions of patients with ASD. As the complexity of ASD, the investigators consider both science-based and clinic-based measurements to ensure no missing of any relevant domain of the complex relations. In addition to the collection of biological factors, the investigators will also collect the comprehensive clinical, environmental, neurocognitive, MRI images to integrate the multiple factors into the matrix features. Finally the investigators will apply the machine learning to provide us the aspects of the underline pathway back into the other sample distribution published as the open dataset to verify and adjust the features in order to achieve satisfactory level of the reliability and stability of the algorithms. With Next Generation Sequencing (NGS) technology, the investigators will sequence the whole exome sequencing (WES) (MiSeq System) of approximately 120 ASD probands, 40 unaffecting siblings and 40 healthy controls of Taiwanese Han population to identify ASD-associated transcriptome profiles. The results will be using real-time PCR (qPCR) or conventional Sanger sequencing to verified. The investigators will use both liquid chromatography/time-of-flight mass spectrometry (LC-MS) and gas chromatography/quadrupole mass spectrometry (GC-MS) for a full assessment of a wide range of metabolites with over 820 metabolites. Hence, this 3-year proposal consists two main parts - the ASD transcriptome sequence analysis by NGS technology and the metabolomics study of ASD via LC-MS and GC-MS technology.

Detailed Description

Primary Aim: To establish a stable and reliable neurogenesis molecular level pathways and potential pathogenesis mechanisms for ASD by using the machine learning approach of the integrated data of biological variables (NGS data and metabolomics) and the comprehensive clinical, environmental, neurocognitive, and MRI images data.

1. To investigate the majority of candidate risk factors from the multiple domains collected in this project;

2. To apply network-based algorithms (including deep learning) to approach the underlining pathogenesis mechanism of ASD;

3. To further verify the machine learning algorithm based on the data collected in this project through other open access database for stability and reliability of our algorithm.

Secondary Aims:

Aim I: To identify the ASD biomarkers and disease mechanism using NGS technology.

1. To investigate the transcriptome profiles occurring in ASD patients;

2. To identify ASD-associated exome sequence variations from a network biology perspective;

3. To identify ASD-associated gene-gene interaction sub-networks; and

4. To explore how the sequencing outcomes, regulate and interact with brain structure and function even linking to neuropsychological functions and behavioral phenotypes.

Aim II: To characterize ASD-affected metabolites.

1. By using LC-MS and GC-MS, we will perform metabolomics analysis, including targeted and untargeted analysis;

2. To identify the potential metabolomics profiles and pathways related to behavioral phenotypes, neuropsychological functions, neuroanatomy and brain functions in patients with ASD; and

3. To identify how the metabolites variance distributions are manipulated through the genetic expressions.

Conditions

Autism Spectrum Disorder

Study Design

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

Study Groups

ASD group

120 patients with clinical diagnosis of ASD according to the DSM-5 diagnostic criteria

Psychiatric diagnosis

Intervention Type: OTHER

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

Unaffected siblings of ASD

40 unaffected siblings of ASD probands

Psychiatric diagnosis

Intervention Type: OTHER

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

TD group

40 healthy age/gender-matched TD controls according to age and neighborhood distribution of the ASD group after interviewed by the Chinese K-SADS-E-DSM-5

Psychiatric diagnosis

Intervention Type: OTHER

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

Interventions

Psychiatric diagnosis

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

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• a clinical diagnosis of ASD defined by the DSM-5 made by board-certificated child psychiatrists at the first visit and following visits
• ages range from 3 to 20
• at least one biological parent
• parents that are both Taiwanese
• subjects and their biological parents consent to participate in this study for complete phenotype assessments and blood withdraw for this study.

Exclusion Criteria

• schizophrenia
• schizoaffective disorder
• organic psychosis.
• Probands with fragile X, intellectual disability, epilepsy, ADHD, and autoimmune diseases will be noted.

• Minimum Eligible Age: 3 Years
• Maximum Eligible Age: 20 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

Countries

Taiwan

Other Identifiers

201801044RINC

Identifier Type: -

Identifier Source: org_study_id