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Machine learning-driven blood biomarker profiling and EGCG intervention in fetal alcohol spectrum disorder
Anna Ramos-Triguero1,2,3,4, Elisabet Navarro-Tapia1,5, Melina Vieiros1,2,3
1Grup de Recerca Infancia i Entorn (GRIE), Institut d'investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
Insights
This study identified seven serum biomarkers for early fetal alcohol spectrum disorder (FASD) diagnosis using machine learning. Epigallocatechin gallate (EGCG) showed potential in restoring neuroinflammatory markers in children with FASD.
Area of Science:
- Neuroscience
- Immunology
- Biochemistry
Background:
- Fetal alcohol spectrum disorder (FASD) is a leading cause of preventable neurodevelopmental disabilities.
- Diagnosis is challenging due to symptom variability and lack of definitive tests.
- Prenatal alcohol exposure (PAE) triggers neuroinflammation and immune dysregulation.
Purpose of the Study:
- Identify novel serum biomarkers for early FASD diagnosis.
- Develop machine learning models for improved diagnostic accuracy.
- Investigate the therapeutic potential of epigallocatechin gallate (EGCG) in FASD.
Main Methods:
- Luminex immunoassays analyzed serum samples from FASD patients.
- Seven key biomarkers (IL-10, IFNγ, CCL2, NGFβ, IL-1β, CX3CL1, CXCL16) were identified.
- Random Forest machine learning model achieved high diagnostic performance (AUC 0.88).
Main Results:
- Biomarkers reflect neuroinflammation and immune dysregulation central to FASD.
- EGCG treatment in a pilot study normalized levels of key inflammatory markers (IFNγ, CX3CL1, IL-1β, IL-10, NGFβ).
- EGCG treatment also suggested promotion of neurogenesis.
Conclusions:
- Serum biomarkers combined with machine learning offer a promising avenue for early FASD diagnosis.
- EGCG demonstrates potential as an intervention for neurodevelopmental and mental health issues in FASD.
- Further research is warranted to validate EGCG's therapeutic efficacy.
Abstract:
Fetal alcohol spectrum disorder (FASD) is a complex neurodevelopmental condition caused by prenatal alcohol exposure (PAE), often underdiagnosed due to heterogeneous symptoms and diagnostic challenges. This study aimed to identify serum-based biomarkers for early FASD diagnosis and assess the potential of epigallocatechin gallate (EGCG), a natural antioxidant found in green tea, in modulating markers related to FASD. Luminex immunoassays were employed to analyze serum samples from FASD patients, identifying seven predictive biomarkers involved in neuroinflammation and immune dysregulation: IL-10, IFNγ, CCL2, NGFβ, IL-1β, CX3CL1, and CXCL16. These biomarkers reflect key disruptions in brain health, particularly in neuroinflammation, which contributes to the cognitive, behavioral, and mental health challenges frequently observed in FASD patients, including memory deficits, attention problems, and emotional dysregulation. To enhance diagnostic precision, machine learning (ML) models were trained on these biomarker datasets, with Random Forest (RF) achieving the highest accuracy (0.89), sensitivity (0.92), specificity (0.83), and ROC AUC (0.88). Additionally, an open-label pilot study in children diagnosed with FASD showed significant restoration of the levels of IFNy, CX3CL1, IL-1β, IL-10, and NGFβ after 12 months of EGCG treatment, suggesting its potential role in mitigating neuroinflammatory responses and promoting neurogenesis. These findings underscore the value of integrating serum biomarkers with ML-driven approaches to advance FASD diagnostics, while also identifying EGCG as a promising intervention for neurodevelopmental and mental health impairments associated with the disorder.
