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The aim of the research is threefold: (1) to identify microbial stool biomarkers associated with Autism Spectrum Disorder (ASD) and eczema, (2) develop risk prediction model for ASD and eczema and (3) explore the effect of various microbiome modulation approaches for amelioration of ASD and eczema.


With the mother-baby cohort, we will be able to generate valuable mega data and huge microbiome reservoir. Using the Big Data Discovery platform, we can identify unique microbial markers that are associated with childhood conditions. Besides, the microbiome reservoir allows us to increase the precision of diagnostic and therapeutic innovations, and product development through purification, isolation and characterization. The product development cycle involves pre-clinical and clinical testing as well as validations in different populations. New data generated will go into our research database to enable iterative improvement.

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Establish mother-baby

Establish mother-baby

Generate microbiome data

Use of Big Data Discovery Platform (bioinformatics, machine learning & artificial intelligence)

Compare and identify microbial markers

Generate microbiome reservoir

Through purification, isolation & characterization

Increase precision of diagnostic and therapeutic innovations & product development

Product Development Cycle

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Research Plan

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Exploration of the roles of gut microbiota in the clinical phenotype of Autism Spectrum Disorder (ASD)
By sampling a large representative clinic-based ASD cohort with detailed phenotyping of their symptoms and cognitive profile, exploration of the role of microbiota in determining the clinical severity will be conducted. This will hint the underlying biomechanism of the microbiota-gut-brain axis in ASD.

Identification of gut microbial markers of ASD

We will do validation in this large representative clinic-based ASD cohort to evaluate the predictability of these biomarkers in the diagnosis of ASD.
Understanding the biomechanism of microbiota-gut-brain axis in ASD
To deepen the understanding of the microbiota-gut-brain axis in ASD, the metabolites of the gut microbiome and their association with the immune status and circulating metabolites of the ASD individuals will be investigated.

Developing a machine learning model for the prediction/early detection of ASD

Our proposed diagnostics for ASD capitalizes on the microbiota-gut-brain axis to generate a risk profile based on the state-of-the-art metagenomic sequencing of gut microbiome and machine-learning algorithms derived from the sizeable clinic and community-based cohorts of ASD and neurotypical children. As perturbation of gut microbiota takes place early in the course of illness, this diagnostic can overcome the current obstacles to achieve prediction from early developmental age.
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Launching a risk prediction test for early detection of ASD in the community
Younger siblings of known ASD children with gut microbiome profiled since infancy will be follow-up to evaluate the presence of ASD at age 4-5. The predictive value of early gut microbial composition on risk of developing ASD will be evaluated, which will inform the use of gut microbiota as an early biomarker of ASD.

Development of microbiome modulation services for ASD

Microbiome modulation such as through the means of prebiotics, probiotics and even fecal microbiota transplant may have beneficial effect to the gastrointestinal and even behavioral symptoms in ASD. Trials will be conducted to evaluate the clinical efficacy in the Chinese population and to understand the mechanisms of different modules to guide subsequent development of specific modulatory treatment.
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