Multi-Omics Analysis Of Morbid Obesity Using A Patented Unsupervised Machine Learning Platform: Genomic, Biochemical, And Glycan Insights
Morbid obesity is a complex health challenge often linked to inflammation and metabolic problems. A recent study explored a new way to tackle this by looking at a person’s unique biological makeup through “multi-omics” analysis. This means examining various biological data, including their genes (genomic), body chemistry (biochemical), and the sugar structures attached to their proteins (glycans).
Researchers used a special artificial intelligence (AI) platform to analyze this comprehensive data and create personalized dietary recommendations for individuals with morbid obesity. Before the intervention, participants showed signs of insulin resistance and chronic inflammation, and their “biological age,” as measured by a GlycanAge index, was significantly higher than their actual chronological age, suggesting accelerated aging.
After following these AI-generated personalized diets for six months, participants experienced significant reductions in both their body mass index (BMI) and their GlycanAge index. This suggests that integrating diverse biological information with advanced machine learning can lead to more effective, tailored strategies for managing obesity and potentially reversing some of its associated health impacts, like accelerated biological aging. This approach offers a promising path toward precision medicine in obesity management.
Source: link to paper